Publication

@inproceedings{Globecom2023_Taya,

title = {Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs},

author = {Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2023},

date = {2023-12-04},

urldate = {2023-12-04},

booktitle = {Proceedings of IEEE Global Communications (GLOBECOM) 2023},

publisher = {IEEE},

address = {Kuala Lumpur, Malaysia},

series = {GLOBECOM 2023},

abstract = {Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-hop federated distillation (CMFD) proposed in the authors' previous work makes neural network (NN) models get close with others in a function space rather than in a parameter space. Hence, this study solves two unresolved challenges of CMFD: (1) communication cost reduction and (2) visualization of model convergence. Based on a proposed dynamic communication cost reduction method (DCCR), the amount of data transferred in a network is reduced; however, with a slight degradation in the prediction accuracy. In addition, a technique for visualizing the distance between the NN models in a function space is also proposed. The technique applies a dimensionality reduction technique by approximating infinite-dimensional functions as numerical vectors to visualize the trajectory of how the models change by the distributed learning algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3600100.3626263,

title = {A Trajectory Estimation Method from Spatially Sparse and Noisy Beacon Data Based on Spring Dynamics},

author = {Junya Maruyama and Yudai Honma and Yuuki Nishiyama and Yoshihiro Kawahara},

url = {https://doi.org/10.1145/3600100.3626263

https://buildsys.acm.org/2023/},

doi = {10.1145/3600100.3626263},

isbn = {9798400702303},

year  = {2023},

date = {2023-11-15},

urldate = {2023-01-01},

booktitle = {Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},

pages = {286–287},

publisher = {Association for Computing Machinery},

address = {Istanbul, Turkey},

series = {BuildSys '23},

abstract = {Analysis of trajectory data within buildings offers insights for optimizing environmental design and habitability. However, data from indoor location sensors tend to be sparse and noisy. This makes it difficult for conventional route estimation models to be applied effectively. Our study seeks to derive detailed, temporally, and spatially rich trajectory data from this compromised sensor information. We achieve this by interpreting trajectories as continuous stay points. To facilitate this, we introduce a building corridor network that conceptualizes buildings as a series of points. Routes are inferred using a sequence estimation model applied to this network. This approach employs spring dynamics, which balance the resistance to staying with the attraction to specific beacons, via mathematical optimization. Notably, our model can deduce a trajectory of 131 points from only 15 beacons with, an accuracy rate of 87. Our method presents a promising avenue for capturing extensive route data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3610661.3617153,

title = {Assessing Infant and Toddler Behaviors through Wearable Inertial Sensors: A Preliminary Investigation},

author = {Ayaka Onodera and Riku Ishioka and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3610661.3617153},

doi = {10.1145/3610661.3617153},

isbn = {9798400703218},

year  = {2023},

date = {2023-10-13},

urldate = {2023-01-01},

booktitle = {Companion Publication of the 25th International Conference on Multimodal Interaction},

pages = {16–20},

publisher = {Association for Computing Machinery},

address = {Paris, France},

series = {ICMI '23 Companion},

abstract = {Accurately assessing a child’s health status and taking appropriate action in case of illness or other emergencies is crucial to recording and sharing a childcare situation, such as sleeping hours, amount of exercise, and timing of meals. Although numerous applications and systems have been proposed to assist in recording and sharing these records, the process is still performed manually, representing a significant burden for parents. Therefore, automatic recording of infants’ and toddlers’ daily activities is required. Moreover, existing automatic infant behavior recognition methods have significant limitations on the space and target of application. In this study, we implement a machine learning model to investigate and propose a method to classify typical daily behaviors of infants and toddlers using a chest-mounted low-sampling rate accelerometer. In particular, the proposed method classifies eight activities: sleeping, crawling, walking, standing, sitting, drinking milk, eating baby food, and holding by a caregiver. As a dataset, we collected accelerometer data for nearly 18 hours with reference videos from ten infants and toddlers between 6 and 24 months. Based on the data, we extracted 60 time- and frequency-domain features calculated from the single accelerometer and a user feature to recognize the target daily activities. The performance evaluation considering different window sizes and machine learning models showed that our classification model reaches nearly 80% accuracy in the best-case scenario.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3594739.3612874,

title = {Smartwatch-Based Sensing Framework for Continuous Data Collection: Design and Implementation},

author = {Yuuki Nishiyama and Kaoru Sezaki},

url = {https://ubicomp-mental-health.github.io/papers/2023/Smartwatch-Based%20Sensing%20Framework%20for%20Continuous%20Data%20Collection_%20Design%20and%20Implementation.pdf

https://www.yuukinishiyama.com/wp-content/uploads/2023/10/UbiComp2023_workshop_talk_compressed.pdf

https://ubicomp-mental-health.github.io/},

doi = {10.1145/3594739.3612874},

isbn = {9798400702006},

year  = {2023},

date = {2023-10-08},

urldate = {2023-10-08},

booktitle = {Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing},

pages = {620–625},

publisher = {Association for Computing Machinery},

address = {Cancun, Quintana Roo, Mexico},

series = {UbiComp/ISWC '23 Adjunct},

abstract = {Smartwatches are an increasingly popular technology that employs advanced sensors (e.g., location, motion, and microphone) comparable to those used by smartphones. Passive mobile sensing, a method of acquiring human behavior data from mobile and wearable devices inconspicuously, is widely used in research fields related to behavior analysis. In combination with machine learning, passive mobile sensing can be used to interpret various human and environmental contexts without requiring user intervention. Because smartwatches are always worn on the wrist, they have the potential to collect data that cannot be collected by smartphones. However, the effective use of smartwatches as platforms for passive mobile sensing poses challenges in terms of battery life, storage, and communication. To address these challenges, we designed and implemented a tailored framework for off-the-shelf smartwatches. We evaluated power consumption under eight different sensing conditions using three smartwatches. The results demonstrate that the framework can collect sensor data with a battery life of 16-31 h depending on the settings. Finally, we considered potential future solutions for optimizing power consumption in passive sensing with off-the-shelf smartwatches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wowmon2023_han,

title = {HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://coe.northeastern.edu/Groups/wowmom2023/index.html},

doi = {10.1109/WoWMoM57956.2023.00043},

year  = {2023},

date = {2023-07-12},

urldate = {2023-07-12},

booktitle = {2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},

pages = {281-289},

address = {Boston, Massachusetts},

abstract = {Distracted riding behavior is one of the main causes of bicycle-related traffic accidents, resulting in a large number of casualties and economic losses every year. There is an urgent need to address this problem by accurately detecting distracted riding behaviors. Inspired by the observation that distracted riding behaviors induce unique head motion features that respond to the rider's attention, we present the HeadSense, a helmet-based system that not only monitors the visual search episode of the rider but also detects distracted riding behaviors. Specifically, HeadSense leverages the inertial motion unit (IMU) to recognize distracted behaviors such as using smartphones, attracting to the roadside element, and abreast riding. We designed, implemented, and evaluated HeadSense through extensive experiments. We conducted experiments with 19 participants inside the university's campus. The experimental results show that HeadSense can achieve an overall accuracy of 86.14% while monitoring visual search episodes. Moreover, HeadSense can detect the occurrence of distracted riding behaviors with an average precision of up to 85.04%.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{HCII2023_HaoyuZhuang,

title = {Detecting Hand Hygienic Behaviors In-the-Wild Using a Microphone and Motion Sensor on a Smartwatch},

author = {Haoyu Zhuang and Liqiang Xu and Yuuki Nishiyama and Kaoru Sezaki},

editor = {Norbert A. Streitz and Shiníchi Konomi},

doi = {10.1007/978-3-031-34609-5_34},

isbn = {978-3-031-34609-5},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

booktitle = {Distributed, Ambient and Pervasive Interactions},

pages = {470--483},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In recent years, the emergence of the COVID-19 pandemic has led to new viral variants, such as Omicron. These variants are more harmful and impose more restrictions on people’s daily hygiene habits. Therefore, during the COVID-19 pandemic, it is logical to automatically detect epidemic protective behaviors without user intent. In this study, we used multiple sensor data from an off-the-shelf smartwatch to detect several defined behaviors. To increase the utility and generalizability of the research results, we collected audio and inertial measurement unit (IMU) data from eight participants in real environments over a long period. In the model-building process, we first created a binary classification between hand hygiene behaviors(hand washing, disinfection, and face-touching) and daily behavior. Then, we distinguished between specific hand hygiene behaviors based on audio and IMU. Ultimately, our model achieves 93% classification accuracy for three behaviors(Hand washing, face touching, and disinfection). The results prove that the accuracy of the classification of behaviors has improved remarkably, which also emphasizes the feasibility of recognizing hand hygiene behaviors using inertial acoustic data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{percom2023_han,

title = {HeadMon: Head Dynamics Enabled Riding Maneuver Prediction},

author = {Zengyi Han and Liqiang Xu and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.percom.org/PerCom2023/},

doi = {10.1109/PERCOM56429.2023.10099215},

year  = {2023},

date = {2023-03-13},

urldate = {2023-03-13},

booktitle = {2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)},

pages = {22-31},

publisher = {IEEE},

address = {Atlanta, USA},

abstract = {Although micro-mobility brings convenience to modern cities, they also cause various social problems, such as traffic accidents, casualties, and substantial economic losses. Wearing protective equipment has become the primary recommendation for safe riding. However, passive protection cannot prevent the occurrence of accidents. Thus, timely predicting the rider's maneuver is essential for active protection and providing more time to avoid potential accidents from happening. Through the qualitative study, we argue that we can use the rider's head dynamic as an information source to predict the rider's following maneuvers. We accordingly present HeadMon, a riding maneuver prediction system for safe riding. HeadMon utilizes the head dynamics of a rider by installing an inertial measurement unit on the helmet. It uses the extracted head dynamics features as the input of the deep learning architecture to achieve prediction. We implemented the HeadMon prototype on Android smartphone as a proof of concept. Through comprehensive experiments with 20 participants, the result demonstrates the excellent performance of HeadMon: not only could it achieve an overall precision of at least 85% for maneuver prediction under a 4s prediction time gap, but it also could keep a high accuracy under a low sampling rate. The low-cost feature of HeadMon allows it to be readily deployable and towards more safety riding.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{published_papers/40226624,

title = {Cooperative Local Distributed Machine Learning Considering Communication Latency and Power Consumption},

author = {Shota Ono and Taku Yamazaki and Takumi Miyoshi and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/CCNC51644.2023.10060678},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (IEEE CCNC 2023)},

pages = {678-679},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{published_papers/40226623,

title = {Enabling Block Transmission on Backoff-based Opportunistic Routing},

author = {Eri Hosonuma and Yuuki Nishiyama and Kaoru Sezaki and Takumi Miyoshi and Taku Yamazaki},

doi = {10.1109/CCNC51644.2023.10059637},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (IEEE CCNC 2023)},

pages = {889-890},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10200773,

title = {Opportunistic Division and Allocation of Machine Learning Task for WSN},

author = {Eri Hosonuma and Nobuyuki Tanaka and Takuma Yamazaki and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki and Takumi Miyoshi and Taku Yamazaki},

doi = {10.1109/ICUFN57995.2023.10200773},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)},

pages = {58-60},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_suzuki,

title = {Poster abstract: Room Scale Localization Improvement Utilizing Stay Time Characteristics of Each Room},

author = {Ryoto Suzuki and Yuuki Nishiyama and Hiroaki Murakami and Yoshihiro Kawahara and Kaoru Sezaki},

doi = {10.1145/3560905.3568098},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

pages = {839–840},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

abstract = {Indoor localization technology is one of the most important topics in the fields of Internet of Things (IoT) and ubiquitous computing, and it has attracted much attention in recent years due to its ability to enable a variety of services. Localization methods using Bluetooth or Wi-Fi signal strength can introduce a low-cost location estimation system. However, due to the instability of the received signal strength and signals leaking from adjacent rooms, a simple method based on signal strength alone frequently results in misjudgment, depending on the signal propagation characteristics. In this paper, we propose a method to suppress misjudgment by considering the characteristics of stay time in different rooms. Our proposed method estimates the user state by fitting the distribution of time spent in each room to a Weibull distribution and applying survival analysis. The experimental results suggest that the method will provide more accurate information about the rooms in which users stay.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_ishioka,

title = {Poster abstract: UV index estimation leveraging GNSS sensors on smartphones},

author = {Riku Ishioka and Yuuki Nishiyama and Kota Tsubouchi and Kaoru Sezaki},

doi = {10.1145/3560905.3568053},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

pages = {863–864},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

abstract = {Monitoring the amount of UV irradiance to which individuals are exposed and ensuring that every individual receives the optimal amount has been the subject of extensive research. In previous research, the UV index was estimated using cell phone cameras, light sensors on smartphones, or wearable UV sensors. We propose a method for estimating the UV index using the widespread global navigation satellite system (GNSS) sensors available on smart-phones. In contrast to approaches that require the sensor to be exposed continuously to the irradiance, this method, which leverages GNSS sensors, has the potential advantage of enabling UV index measurement simply by carrying the phone as usual. As a first step in measuring the index using GNSS sensors, GNSS data were collected from cell phones placed at three locations in a single area; the OpenUV API was utilized as a baseline. The proposed method achieved a mean absolute error of 0.1523, which significantly outperformed the baseline.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_sukeda,

title = {Poster abstract: Recursive Queueing Estimation Using Smartphone-based Acoustic Ranging},

author = {Issey Sukeda and Hiroaki Murakami and Yuuki Nishiyama and Hiroaki Murakami and Yoshihiro Kawahara},

doi = {10.1145/3560905.3568097},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

pages = {837–838},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

abstract = {When customers wait their turn to place an order at a street vendor, they often form a spontaneous queue. Due to the presence of passers-by and people standing outside the queue, it is more difficult than one might think to distinguish between those in the queue and those not in the queue. In this paper, we consider a method that uses acoustic ranging to autonomously detect who is in line and in which order, under the condition that all customers have smartphones. The proposed method is unique in that it can distinguish whether a newly arrived user has joined the end of the queue or not by taking cues from the geometric properties of the queue. Our preparatory queueing simulations confirm that 92.5% of the queuers are estimated correctly.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_xu,

title = {Poster abstract: Convolutional Compressed Sensing for Smartphone Acceleration Data Compression},

author = {Liqiang Xu and Yuuki Nishiyama and Masamichi Shimosaka and Kota Tsubouchi and Kaoru Sezaki},

doi = {10.1145/3560905.3568054},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

pages = {810–811},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

abstract = {As intelligent sensing and smartphone technologies have progressed, a huge amount of highly heterogeneous data have come to be stored in smartphones and uploaded to servers for analysis on a daily basis. This has led to vast storage overheads for users and companies. Hence, data compression becomes the most efficient strategy for suppressing the increase in storage overhead. Compressed sensing (CS) technology is one approach to compressing data, but traditional CS-based algorithms are significantly time-consuming and have low reconstruction performance. In light of these drawbacks, this paper proposes a compressed sensing framework that instead takes advantage of the low time cost and adaptive learning capability of deep learning methods, wherein a convolutional neural network (CNN) is used for compressing and reconstructing acceleration data. Our experiments with actual smartphone acceleration data show that the proposed method dramatically improves the reconstruction performance with very little reconstruction time compared with traditional compressed sensing methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mobicovid22_nishiyama,

title = {MOCHA: Mobile Check-in Application for University Campuses Beyond COVID-19},

author = {Yuuki Nishiyama and Hiroaki Murakami and Ryoto Suzuki and Kazusato Oko and Issey Sukeda and Kaoru Sezaki and Yoshihiro Kawahara},

doi = {10.1145/3492866.3557736},

isbn = {978-1-4503-9165-8/22/10},

year  = {2022},

date = {2022-10-18},

urldate = {2022-10-18},

booktitle = {The Twenty-third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},

volume = {MobiHoc '22},

publisher = {ACM},

address = {Seoul, Republic of Korea},

abstract = {Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{healthcom2022_ono,

title = {Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches},

author = {Shota Ono and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://healthcom2022.ieee-healthcom.org/},

doi = {10.1109/HealthCom54947.2022.9982766},

isbn = {978-1-6654-8016-1},

year  = {2022},

date = {2022-10-17},

urldate = {2022-10-17},

booktitle = {2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)},

pages = {107-112},

publisher = {IEEE},

address = {Genoa, Italy},

abstract = {Wearing a mask considerably mitigates the risk of infection from droplets. Automatic detection of whether a person wears a mask in his/her daily life and the type of masks the person wears can provide useful information for various services such as infection risk assessment, just-in-time alerts, and lifelogging. However, such automatic detection is difficult without the use of video processing or specialized equipment. In this study, the motion sensor of a commercially available smartwatch was used to detect the mask-wearing status. An investigation of the acceleration characteristic and an evaluation experiment of the mask-wearing state detection model revealed an accuracy of approximately 90% when specific motions were classified using motion sensors and machine learning. Furthermore, 98% accuracy was achieved when classifying sitting and walking activities.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_han,

title = {A Micro-mobility Sensing System to Portray Riding Styles},

author = {Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Riding style concerns the way a micro-mobility rider chooses to ride, and it plays a significant role in traffic safety. Portraying a rider’s

riding style is a useful way to guide them towards safer riding behaviors, and offer fine-grained information for insurance companies

and bike-sharing companies to provide better services. To this end, we propose a micro-mobility sensing system to portray riding

styles. Utilizing helmet-mounted and bicycle handle-mounted inertial sensors, our sensing system is able to monitor the micro-mobility

movement status, detect the maneuver behaviors, and the safety check condition of riders. With 10 participants’ experimental data,

we present the feasibility of detecting maneuver behavior and recording the data that characterize the rider’s riding style with our

sensing system, and therefore make it a viable addition to the micro-mobility.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_shimojo,

title = {A Preliminary Study for Detecting Visual Search Behaviors During Street Walking Using Earable Device},

author = {Kazuki Shimojo and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.esense.io/earcomp2022/EarComp_2022_Proceedings.pdf},

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Map applications on smartphones are powerful navigation tools for walking among places to visit for the first time and are used widely. On the other hand, checking the application tend to cause trouble on the road such as collisions with people, cars, and objects. To prevent the troubles, we need to detect the walker’s context regarding visual search behaviors and provide appropriate navigation information for the walker. In this paper, we propose a method to detect a walker’s context regarding visual search behaviors by using motion sensors on an earable device. We collected and investigated motion and gaze data from an earable device and gaze tracker respectively during street walking from five participants. Based on the investigation, we create a machine learning model for detecting looking around, smartphone, or normal during walking 

and stopping conditions. Our evaluations show that our models can detect more than 95% of walking and stopping conditions, and 71% of three details conditions during walking, respectively},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smartcomp2022_komatsu,

title = {Toward Measuring Conversation Duration Using a Wristwatch-type Wearable Device},

author = {Yuki Komatsu and Kazuki Shimojo and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://smartcomp.aalto.fi/assets/WIP-posters/1570807018-poster.pdf

https://www.youtube.com/watch?v=Ai72_ikjsQI},

doi = {10.1109/SMARTCOMP55677.2022.00035},

isbn = {978-1-6654-8152-6},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

pages = {150-152},

publisher = {IEEE},

address = {Espoo, Finland},

abstract = {The frequency and duration of social contact, represented by conversation, is positively correlated with our physical and mental health. Therefore, a method that automatically measures social contact can provide insight into people's health conditions and risks. Even though off-the-shelf wristwatch-type wearable devices are widely used in our daily lives and have rich computational resources, they have not been used as a social-contact monitoring tool in everyday conditions. In this study, we propose a system, called Ohanashi, for continuously monitoring conversational events as a means of social contact in daily life, by edge processing on an off-the-shelf smartwatch. To monitor the conversational event, we developed an audio classification model and implemented it as a smartwatch application, which can classify conversation and noise from an audio stream. Our performance evaluation shows that the classification model can classify conversation and noise with more than 86% accuracy in both silent and noisy environments, and the system can monitor conversation events for more than 15 hours on a smartwatch.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smartcomp2022_kasahara,

title = {Detecting Childcare Activities Using an Off-the-shelf Smartwatch},

author = {Yuki Kasahara and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://smartcomp.aalto.fi/assets/WIP-posters/poster_1570812161.pdf},

doi = {10.1109/SMARTCOMP55677.2022.00038},

isbn = {978-1-6654-8152-6},

year  = {2022},

date = {2022-06-20},

urldate = {2022-06-01},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

pages = {159-161},

publisher = {IEEE},

address = {Espoo, Finland},

abstract = {The childcare environment has significantly changed, owing to accelerating women's social advancement and the increasing number of nuclear families. Improving and supporting childcare have become major challenges in current society. Automatically recording and subsequently observing childcare activities can be used for various purposes to support childcare. However, methods to detect childcare activities using off-the-shelf devices have not yet been proposed. This study develops a method to detect childcare activities that parents perform for their babies using an off-the-shelf wearable device. We define nine childcare activities and develop corresponding detection models based on motion-sensor data from a smartwatch. Our evaluation in a laboratory setting resulted in classification performances of 71% (F1: 0.66).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{percom2022_dong,

title = {DoubleCheck: Detecting Single-Hand Cycling with Inertial Measurement Unit of Smartphone},

author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://youtu.be/AW5B-cPdeFI

https://www.percom.org/},

doi = {10.1109/PerComWorkshops53856.2022.9767429},

isbn = {978-1-6654-1647-4},

year  = {2022},

date = {2022-03-21},

urldate = {2022-03-21},

booktitle = {IEEE International Conference on Pervasive Computing and Communications (PerCom)},

publisher = {IEEE},

address = {Pisa, Italy},

abstract = {Riding bikes with only one hand on the handlebar can severely undermine the steering capability of riders and risk road safety. In this study, we propose a first detection framework for monitoring single-hand cycling on bicycle travel, called DoubleCheck. It is based on the premise that riders adapt their body movement during single-hand cycling, which is distinguishable to the sensors even amid noise from the exasperate road surface. The system can detect handlebar-holding under different road conditions using motion signals from a built-in inertial measurement unit (IMU) in a handlebar-mounted smartphone. We implemented the system and invited 10 participants for our evaluation experiment. Our results show that DoubleCheck achieved an F1-score of 0.94 for hand detection, proving its efficacy for real-life implementation to improve road safety.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3498361.3538782,

title = {Head Dynamics Enabled Riding Maneuver Prediction},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3498361.3538782},

doi = {10.1145/3498361.3538782},

isbn = {9781450391856},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services},

pages = {557–558},

publisher = {Association for Computing Machinery},

address = {Portland, Oregon},

series = {MobiSys '22},

abstract = {While micro-mobility brings convenience to the modern city, they also cause various social problems such as traffic accidents, casualties, and huge economic losses. Wearing protective equipment has become the primary recommendation for safe riding, but passive protection cannot prevent accidents from happening after all. Thus, timely predicting the rider's maneuver is essential for more active protection and buying more time to avoid potential accidents from happening. In this poster, we explore the feasibility of using riders' head dynamics to predict their riding maneuvers. Through ten participants' preliminary study, we observed that not only do riders' head movements appear ahead of their maneuvers but also head movement patterns are distinct with different maneuver intentions. We then construct a deep learning network using Long Short Term Memory, achieving 89% of accuracy on maneuver prediction.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-05431-0_23,

title = {Enhancing Self-Protection: What Influences Human’s Epidemic Prevention Behavior during the COVID-19 Pandemic},

author = {Liqiang Xu and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1007/978-3-031-05431-0_23},

doi = {10.1007/978-3-031-05431-0_23},

isbn = {978-3-031-05430-3},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-Being and Health, Art and Creativity: 10th International Conference, DAPI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings, Part II},

pages = {336–351},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

abstract = {Under the circumstance of the rapid spread of the COVID-19 pandemic, enhancing human’s awareness of self-protection is one practical method to slow down the epidemic. In this study, we utilize mobile sensing to track human activity and guide human’s epidemic prevention behavior by gamified feedback techniques by our developed application. Virtually, human’s self-protection awareness is affected by many factors and the measures to enhance people’s self-protection behavior against the epidemic COVID-19 has always been an unresolved issue. In order to search for factors that influence human’s self-protection behavior, we analyzed the relationships between various human activities and the percentage complete of human’s self-protection behavior and we have extracted some more general conclusions from the results. Based on our data analysis results, we also made some proposals to enhance self-protection behavior. Meanwhile, our study illustrates the effectiveness of the method that analyzes human self-protection behavior through mobile sensing. Our study also validates the effectiveness of persuasive technology on human’s self-protection behavior against the COVID-19 pandemic and therefore we advocate enhancing human’s self-protection awareness through external intervention and guidance by smart device.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smc2022_dong,

title = {DoubleCheck: Single-Handed Cycling Detection with a Smartphone},

author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://ieeesmc2022.org/},

doi = {10.1109/SMC53654.2022.9945380},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},

pages = {268-274},

abstract = {Riding bicycles with only one hand on the handlebar can severely undermine the operator’s steering capability and threaten road and transportation safety. Prior studies have exploited motion sensors to detect riding contexts and recognize related behaviors. Nevertheless, they fail to integrate a scheme to account for single-handed riding with elements crucial to danger prevention: awareness of the surroundings, response to danger, and convenient adoption. In this work, we proposed, designed, and implemented DoubleCheck: a smartphone-based real-time framework for cycling hand detection and distraction recognition. The method monitors handlebar holding on different road surfaces and recognizes hazardous distraction activities related to single-handed cycling using motion signals captured by a built-in inertial measurement unit in a handlebar-borne smartphone. It was designed on the premise that single-handed cycling enabled operators to adapt their body movements to different (often distracting) activities. We conducted an evaluation experiment using 22 participants on asphalt and pavement. The results indicate that DoubleCheck achieves an F1-score of 0.96 for hand detection and 0.69 for distraction recognition, demonstrating its efficacy as a candidate rider-safety precautionary measure.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3557915.3560969,

title = {A Plug-in Memory Network for Trip Purpose Classification},

author = {Suxing Lyu and Tianyang Han and Yuuki Nishiyama and Kaoru Sezaki and Takahiko Kusakabe},

url = {https://doi.org/10.1145/3557915.3560969},

doi = {10.1145/3557915.3560969},

isbn = {9781450395298},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},

publisher = {Association for Computing Machinery},

address = {Seattle, Washington},

series = {SIGSPATIAL '22},

abstract = {Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-term objective to enhance travel demand and behavior models used in urban planning. Although studies on this topic have been extensively conducted, most past research preferred relying on traveler attributes or long-term travel histories to achieve accurate results. These data could be privacy sensitive and often do not satisfy real-world scenarios. This study addresses the problem of classifying trip purpose by only space activity information to avoid privacy conflict. 1) External memories are collected from factorized components based on the non-negative Tucker decomposition scheme. 2) These memories are extended by the cross-attention mechanism to achieve feature augmentation. 3) Subsequently, a novel concept called "latent mode alignment" is proposed. By leveraging the linear characteristics of external memories, geographic contextual latent modes are represented and matched with travel activities; this procedure is called älignment." 4) The gate mechanism controls the eventual outputs for update. The proposed plug-in memory network (PMN), combined with baseline models, effectively outperforms the original settings. Moreover, combination models are validated with strong tolerance through missing data tests, which are common and problematic in real-world scenarios. The proposed PMN is a plug-and-play design that is easy to combine with newly developed classification models, and other memory collection methods can be expected.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{igessc2021_peng,

title = {Estimation of Greenhouse Gas Emission Reduction from Shared Micromobility System},

author = {Helinyi Peng and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://youtu.be/vbt622kXNuU},

doi = {10.1109/IGESSC53124.2021.9618701},

issn = {2640-0138},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

booktitle = {2021 IEEE Green Energy and Smart Systems Conference (IGESSC)},

pages = {1-6},

publisher = {IEEE},

address = {Long Beach, CA, USA},

abstract = {Shared micromobility is widely recognized as an environmentally friendly travel mode and a critical component of transportation decarbonization. However, quantitatively assessing its environmental impact using real-world trip data is an unresolved and challenging subject. In this research, we proposed a system combining machine learning algorithms and the Monte Carlo simulation to address this issue. First, several machine learning algorithms (Random Forest, XGBoost, and LightGBM) were utilized to identify citizens’ travel mode choice preferences and then estimate the substituted travel mode of each micromobility trip. Second, to ensure the reliability of the final environmental impact assessment, the Monte Carlo simulations were used to simulate the substituted mode of each trip. Then the environmental impacts were calculated based on the life cycle greenhouse gas emissions. Instead of estimating a specific number, we obtained a probabilistic outcome for environmental impacts by using the Monte Carlo simulation, which considers the uncertainty. According to the studies, the shared bike service and the shared e-scooter service have positive environmental impacts. However, these effects are limited compared to the transportation sector’s total emissions. The most compelling reason is that shared micromobility comprises a minuscule part of total urban travel.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{itsc2021_ntf,

title = {An Unsupervised Learning-based Approach for User Mobility Analysis of E-Scooter Sharing Systems},

author = {Hidenaga Ushijima and Shunsuke Aoki and Peng Helinyi and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://2021.ieee-itsc.org/},

doi = {10.1109/ITSC48978.2021.9564616},

isbn = {978-1-7281-9141-6},

year  = {2021},

date = {2021-10-25},

urldate = {2021-10-25},

booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},

pages = {1425-1430},

publisher = {IEEE},

abstract = {Human mobility analysis is a key method for understanding urban dynamics and mobility optimization. Novel last-mile mobility, called micromo-bilities, that includes shared bicycles, electric bicycles (e-bikes), and electric scooters are seeing rapid widespread acceptance in major cities. Compared with existing mobility data such as cars, buses, and trains, the majority trip distance of micromobilities is short, typically less than a few miles. The riders use them for commuting, sightseeing, shopping, and/or fun. By using the mobility data of micromobilities, we can observe more fine-grained human mobility in urban areas than existing data sources. In this paper, we present an unsupervised learning-based technique to analyze human mobility in urban areas and to study user clusters for such micromobility services. In our approach, we cluster user mobility patterns by using non-negative tensor factorization (NTF) from area-based trip data (which only included locations of origin and destination). Our approach was applied to micromobility data collected from Chicago and Washington, D.C., and we observed characteristic patterns.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{duc_vtc2021b,

title = {A Run-time Dynamic Computation Offloading Strategy in Vehicular Edge Computing},

author = {Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://events.vtsociety.org/vtc2021-fall/},

doi = {10.1109/VTC2021-Fall52928.2021.9625245},

isbn = {2577-2465},

year  = {2021},

date = {2021-09-27},

urldate = {2021-09-27},

booktitle = {2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},

pages = {1-7},

publisher = {IEEE},

address = {Online},

abstract = {In vehicular edge computing (VEC), offloading the tasks to the nearby resource-rich edge servers helps each vehicle enhance computational capabilities and improve in-vehicle applications' performance. However, the concentration of travel at specific spaces and times poses significant challenges on the load-balancing and scheduling of computation tasks at the edge servers. This paper studies a low-complexity dynamic online offloading strategy that efficiently reduces task delay and computing resource consumption in the multi-user, multiserver vehicular edge computing scenarios. Our design addresses issues of computation task placement and execution order of the tasks on each server. We use a realistic approach that vehicles generate tasks over time, and the set of the tasks is unknown in advance so that the offloading decisions are made in runtime. Extensive simulations are conducted on a real mobility trace of Luxembourg city, and the results show that the proposed algorithm effectively improves the offloading utility of the system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3460418.3479294,

title = {Detecting Single-Hand Riding with Integrated Accelerometer and Gyroscope of Smartphone},

author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3460418.3479294},

doi = {10.1145/3460418.3479294},

isbn = {9781450384612},

year  = {2021},

date = {2021-09-21},

urldate = {2021-01-01},

booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {Virtual, USA},

series = {UbiComp '21},

abstract = {Single-hand cycling poses a safety threat with the decrement of riders’ response 

capacity. Recognizing risky behavior by prevalently used smartphones could lead to 

enhanced riding safety. In this work, we propose a single-hand cycling recognition 

method based on motion data acquired from the three-axis accelerometer and gyroscope 

integrated into a handlebar-installed smartphone. We conducted a 4-person experiment. 

The data result demonstrates that motion data of double-hand cycling clearly distinguishes 

from that of single-hand, revealing the chance to materialize a robust detection tool 

in smartphones to enable safer biking. For future work, we prepare to redesign the 

experiment under more sophisticated circumstances with an improved platform, thus 

scaling this sensing method for real-life usage.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3460418.3479283,

title = {Experience Sampling Tool for Repetitive Skills Training in Sports Using Voice User Interface},

author = {Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3460418.3479283},

doi = {10.1145/3460418.3479283},

isbn = {9781450384612},

year  = {2021},

date = {2021-09-21},

urldate = {2021-01-01},

booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},

pages = {54–55},

publisher = {Association for Computing Machinery},

address = {Virtual, USA},

series = {UbiComp '21},

abstract = {Repetitive skills training (RST) is a commonly used method for improving skills. 

Although wearable devices and existing context-aware technologies allow us to easily 

detect objective data during RST, subjective data have not been collected effectively, 

even though both objective and subjective data are important for RST. In this paper, 

we propose and implement a prototype system, called MiQ, to collect subjective data 

with minimum workload during RST for sports. MiQ allows us to record subjective data 

hands-free via a voice user interface (VUI). We also discuss the future scope of the 

proposed prototype system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3460418.3479282,

title = {HeadSense: A Head Movement Detecting System for Micro-Mobility Riders},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3460418.3479282},

doi = {10.1145/3460418.3479282},

isbn = {9781450384612},

year  = {2021},

date = {2021-09-21},

urldate = {2021-01-01},

booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},

pages = {26–27},

publisher = {Association for Computing Machinery},

address = {Virtual, USA},

series = {UbiComp '21},

abstract = {Head movement for traffic visual searching, is one of the important factors in traffic 

safety. In this paper, we present the design, implementation, and preliminary evaluation 

of the HeadSense, a helmet device that detects the head movement of micro-mobility 

rider. HeadSense is capable of generating data streams using the embedded 9-axis inertial 

measurement unit (IMU) sensor. After the process of segmentation and classification 

algorithm, HeadSense can automatically detect an individual’s head movement sequence 

and visual search episodes, across the rider’s entire riding journey. Experiments 

with 5 participants show that our system achieves 94.7% for per-second level detection 

and 80.59% F1-score for per-episode level detection.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ieee_itsc_mimosense,

title = {MiMoSense: An Open Crowdsensing Platform for Micro-Mobility},

author = {Zengyi Han and Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://2021.ieee-itsc.org/},

doi = {10.1109/ITSC48978.2021.9564524},

year  = {2021},

date = {2021-09-19},

urldate = {2021-09-19},

booktitle = {2021 IEEE International Conference on Intelligent Transportation (ITSC)},

pages = {1-6},

publisher = {IEEE},

abstract = {The use of micro-mobility (e.g., bicycle and scooter) and their data for urban sensing and rider assessment is becoming increasingly popular in research. However, different research topics require different sensor setups; no general data collecting tools for the micro-mobility makes the researcher who wishes to collect data has to build their own collecting system from scratch. To this end, we present MiMoSense, an open crowdsensing platform for micro-mobility. MiMoSense consists of two components: (1) MiMoSense server, which is set up on the cloud, and used to manage sensing studies and the collected data for research and sharing. (2) MiMoSense client, uses micro-mobility carrying various sensors and IoT devices to collect multiple kinds of data during traveling. As a reusable open-source software, MiMoSense shifts the researcher's focus from software development to sensing data analysis; it can help researchers quickly develop an extensible platform for collecting micro-mobility's raw sensing data and inferring traveling context. We have evaluated MiMoSense's battery consumption, message latency and discuss its use.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ieice2021_duc,

title = {An Online Task Offloading Strategy in Vehicular Edge Computing},

author = {Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},

url = {http://www.ieice-taikai.jp/2021society/jpn/

https://www.ieice.org/~icm/jpn/award/sub/awardees.html},

year  = {2021},

date = {2021-09-14},

urldate = {2021-09-14},

booktitle = {IEICE Society Conference 2021},

publisher = {IEICE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{edgedl2021-uv,

title = {Towards Estimating UV Exposure Using GPS Signal Strength from a Carrying Smartphone},

author = {Soichiro Higuma and Kosuke Hatai and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.smart-comp.info/},

doi = {10.1109/SMARTCOMP52413.2021.00063},

isbn = {2693-8340},

year  = {2021},

date = {2021-08-23},

urldate = {2021-08-23},

booktitle = {2021 IEEE International Conference on Smart Computing (SMARTCOMP)},

pages = {299-304},

publisher = {IEEE},

address = {Irvine, Orange County, California, USA (Virtual Workshop)},

abstract = {Owing to lifestyle changes, urbanization, and the COVID-19 pandemic, many people spend more time indoors and tend to receive less direct sunlight than before. Although excessive or inadequate ultraviolet (UV) exposure can be harmful to our physical and mental health, moderate UV exposure is essential for vitamin D (VD) production in the body. In this study, we estimate the UV exposure using an off-the-shelf smartphone, and explore the relationship between the UV values and GPS signal strength (C/N0). The results demonstrate that a strong correlation (R 2 = 0.73) between the UV values and carrier to noise density (C/N0) even if the smartphone and UV sensor are moved. Therefore, it is possible to estimate the UV exposure to some extent from a person's location, even while carrying a smartphone.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{taxi_co2_20201,

title = {Towards Infectious Disease Risk Assessment in Taxis using Environmental Sensors},

author = {Hidenaga Ushijima and Shota Ono and Yuuki Nishiyama and Kaoru Sezaki},

editor = {Norbert Streitz and Shiníchi Konomi},

url = {http://2021.hci.international/},

doi = {10.1007/978-3-030-77015-0_13},

isbn = {978-3-030-77015-0},

year  = {2021},

date = {2021-07-07},

urldate = {2021-07-07},

booktitle = {Distributed, Ambient and Pervasive Interactions},

volume = {12782},

pages = {178--188},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The spread of Coronavirus disease of 2019 (COVID-19) has reaffirmed the importance of ventilation in enclosed public spaces. Studies on air quality in public spaces such as classrooms, hospitals, and trains have been conducted in the past. However, the interior of a taxi, where an extremely small space is shared with an unspecified number of people, has not been sufficiently studied. This is a unique environment where ventilation is important. This study compared ventilation meth-ods focusing on the CO2 concentration in the cabin, and evaluated the frequency of ventilation in an actual taxi using sensing technology},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2020_selfguard,

title = {SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic},

author = {Yuuki Nishiyama and Takuro Yonezawa and Kaoru Sezaki},

url = {http://sensys.acm.org/2020/

https://youtu.be/KYmvCHl_U7g},

doi = {10.1145/3384419.3430592},

isbn = {9781450375900},

year  = {2020},

date = {2020-11-16},

urldate = {2020-11-16},

booktitle = {Proceedings of the 18th Conference on Embedded Networked Sensor Systems, Virtual Event, Japan},

number = {2},

pages = {780–781},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '20},

abstract = {Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.},

key = {self-tracking, mobile sensing, GPS, COVID-19},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3410530.3414434,

title = {Towards Estimating UV Light Intensity Using GPS Signal Strength},

author = {Soichiro Higuma and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3410530.3414434},

doi = {10.1145/3410530.3414434},

isbn = {9781450380768},

year  = {2020},

date = {2020-09-13},

urldate = {2020-09-13},

booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},

pages = {684–687},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Mexico},

series = {UbiComp-ISWC '20},

abstract = {Due to recent urbanization and changing lifestyles, many people have been spending more time indoors. Hence, they tend to receive less direct sunlight than ever before. Although excessive/inadequate UV exposure can be harmful to human health leading to illnesses such as skin cancer, spots, or depression, moderate UV exposure is necessary for vitamin D production in the body. Therefore, estimating UV exposure with a commonly used device is useful for maintaining a healthy lifestyle from excessive/inadequate UV exposure in our daily life. In this study, we aim to estimate UV exposure, and to this end, we used the GPS signal strength (C/No) collected from an off-the-shelf smartphone for exploring the relationship between UV values and C/No. We conducted an experiment and measured UV values and C/No from 10 places in two different situations. From the results, we observed a significant correlation (R2 more than 0.87) between UV values and C/No when all the data were divided by the sun/shade condition. This result supports the fact that UV values potentially can be inferred from C/No to some degree if the sun/shade condition can be detected.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3410530.3414369,

title = {Using IOS for Inconspicuous Data Collection: A Real-World Assessment},

author = {Yuuki Nishiyama and Denzil Ferreira and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

url = {https://doi.org/10.1145/3410530.3414369

https://www.yuukinishiyama.com/wp-content/uploads/2020/09/AWARE_HASCA2020_UbiComp2020.pdf},

doi = {10.1145/3410530.3414369},

isbn = {9781450380768},

year  = {2020},

date = {2020-09-12},

urldate = {2020-09-12},

booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},

pages = {261–266},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Mexico},

series = {UbiComp-ISWC '20},

abstract = {Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-50344-4_17,

title = {iOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform},

author = {Yuuki Nishiyama and Denzil Ferreira and Yusaku Eigen and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

editor = {Norbert Streitz and Shiníchi Konomi},

url = {https://www.yuukinishiyama.com/2020/07/25/hcii2020/

https://www.yuukinishiyama.com/wp-content/uploads/2020/07/AWARE-iOS_HCII2020_preprint.pdf

https://github.com/tetujin/AWAREFramework-iOS},

doi = {10.1007/978-3-030-50344-4_17},

isbn = {978-3-030-50344-4},

year  = {2020},

date = {2020-07-10},

urldate = {2020-07-10},

booktitle = {Distributed, Ambient and Pervasive Interactions},

volume = {12203},

pages = {223--243},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chenwei2020_LOIS,

title = {Mobile Sensing of Pedestrian Mobility and its Assessment},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.ieice.org/ken/paper/20200312h1vo/eng/},

isbn = {0913-5685},

year  = {2020},

date = {2020-03-04},

booktitle = {IEICE Tech. Rep.},

volume = {119},

number = {477},

pages = {121--126},

publisher = {IEICE Technical Committee},

organization = {Life Intelligence and Office Information Systems (LOIS)},

abstract = {We propose a client-server system that provides crowd detection and mobility information. Our proposed system has the advantages of low cost and location flexibility without pre-deployed, as long as there is a sufficient number of users involved. We conducted several experiments in real environments to determine the feasibility, accuracy and applicable environment of the system. The result shows that the system can effectively capture the flow of people in the experimental area. In some cases, under the same environment, it can obtain almost the same mobility tracking information from fewer participating users than the GPS method.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3356995.3364541,

title = {Using Mobile Sensing Technology for Capturing People Mobility Information},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3356995.3364541},

doi = {10.1145/3356995.3364541},

isbn = {9781450369640},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility},

pages = {33–40},

publisher = {Association for Computing Machinery},

address = {Chicago, IL, USA},

series = {PredictGIS’19},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3307334.3328719,

title = {Capturing Subjective Time as Context and It’s Applications (Poster)},

author = {Takuro Yonezawa and Yuuki Nishiyama and Kei Hiroi and Nobuo Kawaguchi},

url = {https://doi.org/10.1145/3307334.3328719},

doi = {10.1145/3307334.3328719},

isbn = {9781450366618},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services},

pages = {647–648},

publisher = {Association for Computing Machinery},

address = {Seoul, Republic of Korea},

series = {MobiSys ’19},

abstract = {We propose an integrated framework for sensing, recognizing and utilizing of subjective time as context. Various studies on experimental psychology have showed several factors which affects subjective time. Those factors should be partially captured by ubiquitous sensors such as smartphones and wearable devices, therefore, we tackle to create common and individual model for subjective time based on the sensor data. We report our first prototype implementation for the framework based on AWARE framework with adding experience sampling method for subjective time recognition. In addition, we discuss potential applications which leveraging advantages of subjective time as context.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3338286.3340133,

title = {Challenges of Parkinson’s Disease: User Experiences with STOP},

author = {Elina Kuosmanen and Valerii Kan and Julio Vega and Aku Visuri and Yuuki Nishiyama and Anind K Dey and Simon Harper and Denzil Ferreira},

url = {https://doi.org/10.1145/3338286.3340133},

doi = {10.1145/3338286.3340133},

isbn = {9781450368254},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services},

publisher = {Association for Computing Machinery},

address = {Taipei, Taiwan},

series = {MobileHCI ’19},

abstract = {Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{isokawa2018,

title = {Fish Species Annotation System for Environmental Detection by Marine Life as a Sensor},

author = {Naohiro Isokawa and Wataru Sasaki and Yuuki Nishiyama and Tadashi Okoshi and Jin Nakazawa},

url = {http://hcioutdoors.net/wp-content/uploads/2018/03/Isokawa-fish-species-annotation.pdf},

year  = {2018},

date = {2018-04-21},

booktitle = { HCI Outdoors: A CHI 2018 Workshop on Understanding Human-Computer Interaction in the Outdoor, Canada, Montreal},

abstract = {The ocean was born on the earth 4.6 billion years ago. Life

was born in the ocean, further evolved creatures created

oxygen in the ocean, and that oxygen also changed the atmospheric composition of the Earth. From ancient times life

has received many benefits from the ocean. Even in recent

years, fluctuations in the marine environment are drawing

attention as one of the major environmental problems. Because there is a possibility that it will have a big influence

on human society and ecosystem. Therefore, in the field

of oceanography, research to detect and predict changes

in the marine environment in various ways is actively conducted. We aim to detect changes in the marine environment from the behavior of marine organisms (mainly fish).

In this paper, we implemented a system for annotation of

fish species which is an important module for our research.

This system aims to collect accurate data on fish species by

targeting professional oceanographers. For motivation for

annotation work, the system provides analytical results and

generation models as rewards for work. We also conducted

a questionnaire survey of 14 professors, researchers, and

students specializing in oceanography for annotation system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3267305.3267584,

title = {Meal Photo SNS with Mutual Healthiness Evaluation for Improving Users’ Eating Habits},

author = {Yusaku Eigen and Yuuki Nishiyama and Takuro Yonezawa and Tadashi Okoshi and Jin Nakazawa},

url = {https://doi.org/10.1145/3267305.3267584},

doi = {10.1145/3267305.3267584},

isbn = {9781450359665},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers},

pages = {339–342},

publisher = {Association for Computing Machinery},

address = {Singapore, Singapore},

series = {UbiComp ’18},

abstract = {We propose a Meal Photo SNS (called HealthyStadium) for improving users' eating habits by mutually assessing each others' health. This application's method is to evaluates pictures of meals leads to the realization of sustainable journaling. In addition, we implement competitive awareness that motivates the users to improve their eating habits by allowing users to share their healthiness ranking amongst the users. Through our study, we confirmed that our system enables users to motivate for improving eating habits. Also we found the number of records is increased by our system.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3236112.3236154,

title = {Senbay: A Platform for Instantly Capturing, Integrating, and Restreaming of Synchronized Multiple Sensor-Data Stream},

author = {Yuuki Nishiyama and Anind K Dey and Denzil Ferreira and Takuro Yonezawa and Jin Nakazawa},

url = {https://github.com/tetujin/SenbayKit

http://www.senbay.info/

https://youtu.be/lfUzxinGl24

https://www.yuukinishiyama.com/wp-content/uploads/2020/08/Senbay-MobileHCI2018-demo_preprint.pdf},

doi = {10.1145/3236112.3236154},

isbn = {9781450359412},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct},

pages = {291},

publisher = {Association for Computing Machinery},

address = {Barcelona, Spain},

series = {MobileHCI ’18},

abstract = {The spread of smartphones allows us to freely capture video and diverse hardware sensors' data (e.g., accel erometer, gyroscope). While recording such data is relatively simple, it is often challenging to share and restream this data to other people and applications. Such capability is very valuable for a range of applications such as a context-aware prototyping/developing platform, an integrated data recording and analysis tool, and a sensor-data based video editing system. To enable such complex operations, we propose Senbay, a platform for instant sensing, integrating, and restreaming multiple-sensor data streams. The platform embeds collected sensor data into a video frame using an animated two-dimensional barcode via real-time video processing. The video-embedded sensor data, dubbed Senbay Video, can be easily restreamed to other people and reused by data rich, context-aware applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3282894.3289737,

title = {Mobile-Based Monitoring of Parkinson’s Disease},

author = {Elina Kuosmanen and Valerii Kan and Aku Visuri and Julio Vega and Yuuki Nishiyama and Anind K Dey and Simon Harper and Denzil Ferreira},

url = {https://doi.org/10.1145/3282894.3289737},

doi = {10.1145/3282894.3289737},

isbn = {9781450365949},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia},

pages = {441–448},

publisher = {Association for Computing Machinery},

address = {Cairo, Egypt},

series = {MUM 2018},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3081333.3089324,

title = {Poster: Extensive Evaluation of Emotional Contagion on Smiling Selfies over Social Network},

author = {Wataru Sasaki and Mikio Obuchi and Kazuki Egashira and Naohiro Isokawa and Yuki Furukawa and Yuuki Nishiyama and Tadashi Okoshi and Jin Nakazawa},

url = {https://doi.org/10.1145/3081333.3089324},

doi = {10.1145/3081333.3089324},

isbn = {9781450349284},

year  = {2017},

date = {2017-01-01},

booktitle = {Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services},

pages = {180},

publisher = {Association for Computing Machinery},

address = {Niagara Falls, New York, USA},

series = {MobiSys ’17},

abstract = {We propose "SmileWave", the first selfie social networking service to reveal the existence of emotional cognation through smiling selfies on the social network. We conducted multiple rounds of in-the-wild user studies with 86 cumulative total users for total duration of 5 weeks. Throughout the entire study, we confirmed the occurrence of smile-based emotional contagion over social network, not only in the momentary duration but in longer term period.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7460714,

title = {Poster Abstract: SmileWave - Sensing and Analysis of Smile-Based Emotional Contagion over Social Network},

author = {Wataru Sasaki and Yuki Furukawa and Yuuki Nishiyama and Tadashi Okoshi and Jin Nakazawa and Hideyuki Tokuda},

doi = {10.1109/IPSN.2016.7460714},

isbn = {978-1-5090-0802-5},

year  = {2016},

date = {2016-04-28},

booktitle = {2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)},

pages = {1-2},

publisher = {IEEE},

address = {Vienna, Austria},

abstract = {This paper proposes ''SmileWave", a system for revealing smile-based emotional contagion, propagation effect of the similar emotion through smiley facial expression, on the social network where users interact each other through web-based user interface rather than in-person interaction. SmileWave is a picture-based networking service and detects the change of smile degree when the user looks at posted smile images of others. Our extensive user study with 50 participants for 30 days confirmed the emotional contagion effect on SmileWave. Users' smile degree improved by 27% when the user looked at posted smile images. The result also proved that there is a stronger effect on smile-based emotional contagion when the examinee and the person in the image are in close relationship.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/2938559.2938603,

title = {Poster: NiSleep: Gamification-Friendly Quantitative Evaluation Methodology of Sleep Quality},

author = {Kazuki Egashira and Yuki Furukawa and Yuuki Nishiyama and Tadashi Okoshi and Jin Nakazawa and Hideyuki Tokuda},

url = {https://doi.org/10.1145/2938559.2938603},

doi = {10.1145/2938559.2938603},

isbn = {9781450344166},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion},

pages = {132},

publisher = {Association for Computing Machinery},

address = {Singapore, Singapore},

series = {MobiSys ’16 Companion},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7061042,

title = {A robot control system for video streaming services by using dynamic encoded QR codes},

author = {Masaki Ogawa and Takuro Yonezawa and Yuuki Nishiyama and Jin Nakazawa and Hideyuki Tokuda},

doi = {10.1109/ICMU.2015.7061042},

isbn = {978-4-9076-2612-9},

year  = {2015},

date = {2015-03-16},

booktitle = {2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU)},

pages = {86-87},

publisher = {IEEE},

address = {Hakodate, Japan},

abstract = {We propose a novel robot control system by transmitting robot control information on existing video streaming services as dynamic encoded two-dimentional visual code. We implemented sensor data transmitting system by using dynamic encoded two-dimentional visual code which called SENSe-TREAM [1] and we built the robot controlling system by using SENSeTREAM architecture. This paper shows the architecture of robot controlling system and future vision of telepresence and human-robot interaction.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/2800835.2800847,

title = {Senbay: Smartphone-Based Activity Capturing and Sharing Using Sensor-Federated Video},

author = {Yuuki Nishiyama and Tadashi Okoshi and Yin Chen and Takuro Yonezawa and Jin Nakazawa and Hideyuki Tokuda},

url = {https://doi.org/10.1145/2800835.2800847},

doi = {10.1145/2800835.2800847},

isbn = {9781450335751},

year  = {2015},

date = {2015-01-01},

booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers},

pages = {21–24},

publisher = {Association for Computing Machinery},

address = {Osaka, Japan},

series = {UbiComp/ISWC’15 Adjunct},

abstract = {We present Senbay, a novel smartphone-based platform for capturing and sharing synchronously-recorded video/sensor data stream using animated two-dimensional barcode. In the ubicomp environment, mobile devices have enabled us to capture video and various types of sensor data easily. However, currently we do not have a framework to instantly integrate video and those sensor data on smartphones and share them easily with other users on the net. In this paper, we present Senbay, a platform to integrate video and sensor data stream into single video on smartphones. With Senbay, sensor data stream will be embedded into a video as animated QR code in real-time, thus users can share the "sensor-federated" video easily on the web, through lots of popular video sharing services. This paper introduces the architecture and evaluation of Senbay and discusses future vision of the sensor-federated video sharing platform along with several applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{7001876,

title = {Towards health exercise behavior change for teams using life-logging},

author = {Yuuki Nishiyama and Tadashi Okoshi and Takuro Yonezawa and Jin Nakazawa and Kazunori Takashio and Hideyuki Tokuda},

doi = {10.1109/HealthCom.2014.7001876},

isbn = {978-1-4799-6644-8},

year  = {2014},

date = {2014-10-01},

booktitle = {2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)},

pages = {397-403},

publisher = {IEEE},

address = {Natal, Brazil},

abstract = {Recent technological trends on mobile/wearable devices and sensors have been enabling increasing number of people to collect and store their “life-logs” easily in their daily lives. Beyond exercise behavior change of individual user, our research focus is on the behavior change of teams, based on life-logging technologies and information sharing. In this paper, we propose and evaluate six different types of information sharing model among team members for their exercise promotion, leveraging concepts of “competition” and “collaboration”. According to our experimental mobile web application for exercise promotion and extensive user study among 64 total users for three weeks, the model with “external competition” technique resulted the most effective performance for competitive teams such as sport teams.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dalt_pervasive2012,

title = {DALT: Detection Algorithm of Throwing Form Changing to Prevent the Baseball Player ’s Throwing Related Injuries},

author = {Yuuki Nishiyama and Tomotaka Ito and Takuro Yonezawa and Jin Nakazawa and Kazunori Takashio and Hideyuki Tokuda},

url = {https://sites.google.com/site/ubihealth2012/workshop_program},

year  = {2012},

date = {2012-06-18},

booktitle = {UbiHealth: 6th International Workshop on Ubiquitous Health and Wellness (Part of Pervasive 2012 conference)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}