Publication

@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{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{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}

}