Journals
[J1]
Yuuki Nishiyama, Subaru Atsumi, Kota Tsubouchi, Kaoru Sezaki
A-UVI: GNSS-Assisted EO-based UV Index Estimation Method for Individual-level Precise UV Exposure Assessment Journal Article Open Access
In: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 9 (2), 2025.
@article{10.1145/3729463,
title = {A-UVI: GNSS-Assisted EO-based UV Index Estimation Method for Individual-level Precise UV Exposure Assessment},
author = {Yuuki Nishiyama and Subaru Atsumi and Kota Tsubouchi and Kaoru Sezaki},
url = {https://dl.acm.org/doi/10.1145/3729463},
doi = {10.1145/3729463},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
volume = {9},
number = {2},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Excessive or insufficient exposure to ultraviolet (UV) light can have adverse effects on health, including the development of skin cancer, cataracts, and osteoporosis. An Earth observation (EO)-based UV index can estimate area-level UV indexes without effort in open-sky environments but can not provide sufficient accuracy for shaded environments. In contrast, conventional methods for monitoring individual-level, i.e., personal, UV exposure, such as mobile and wearable UV sensors, face limitations in terms of measurement and usability, presenting challenges for practical long-term usage. To address these issues, we introduce A-UVI, a method that enhances the accuracy of the EO-based UV index by leveraging raw signals from global navigation satellite systems (GNSS). By integrating this EO-based UV index and an attenuation ratio estimated from raw GNSS signals, our method especially improves estimation accuracy in shady environments affected by obstructions. We evaluated our method on data collected by different GNSS receivers in different mobility scenarios encompassing a diverse range of contexts and observation areas over the course of three days. Our evaluation showed that A-UVI estimates the UV index with a precision exceeding existing methods by at least 44.25%, achieving 5.53 times higher estimation accuracy in forest environments. We also confirmed that A-UVI is compatible with GNSS receivers in consumer-grade smartphones and has an average accuracy that is 23% better than the baseline EO-based method. Our findings demonstrate that utilizing raw GNSS signals enables accurate estimation of the UV index in various conditions, including in shaded areas, without the need for particular measurement actions or devices. This marks a significant advancement in enabling passive individual-level UV exposure monitoring and adaptive UV exposure management beyond simple exposure tracking.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Excessive or insufficient exposure to ultraviolet (UV) light can have adverse effects on health, including the development of skin cancer, cataracts, and osteoporosis. An Earth observation (EO)-based UV index can estimate area-level UV indexes without effort in open-sky environments but can not provide sufficient accuracy for shaded environments. In contrast, conventional methods for monitoring individual-level, i.e., personal, UV exposure, such as mobile and wearable UV sensors, face limitations in terms of measurement and usability, presenting challenges for practical long-term usage. To address these issues, we introduce A-UVI, a method that enhances the accuracy of the EO-based UV index by leveraging raw signals from global navigation satellite systems (GNSS). By integrating this EO-based UV index and an attenuation ratio estimated from raw GNSS signals, our method especially improves estimation accuracy in shady environments affected by obstructions. We evaluated our method on data collected by different GNSS receivers in different mobility scenarios encompassing a diverse range of contexts and observation areas over the course of three days. Our evaluation showed that A-UVI estimates the UV index with a precision exceeding existing methods by at least 44.25%, achieving 5.53 times higher estimation accuracy in forest environments. We also confirmed that A-UVI is compatible with GNSS receivers in consumer-grade smartphones and has an average accuracy that is 23% better than the baseline EO-based method. Our findings demonstrate that utilizing raw GNSS signals enables accurate estimation of the UV index in various conditions, including in shaded areas, without the need for particular measurement actions or devices. This marks a significant advancement in enabling passive individual-level UV exposure monitoring and adaptive UV exposure management beyond simple exposure tracking.
[J2]
Elina Kuosmanen, Florian Wolling, Julio Vega, Valerii Kan, Yuuki Nishiyama, Simon Harper, Kristof Van Laerhoven, Simo Hosio, Denzil Ferreira
Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness Journal Article Open Access
In: JMIR Mhealth Uhealth, 8 (11), pp. e21543, 2020, ISSN: 2291-5222.
@article{info:doi/10.2196/21543,
title = {Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness},
author = {Elina Kuosmanen and Florian Wolling and Julio Vega and Valerii Kan and Yuuki Nishiyama and Simon Harper and Kristof Van Laerhoven and Simo Hosio and Denzil Ferreira},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33242017},
doi = {10.2196/21543},
issn = {2291-5222},
year = {2020},
date = {2020-11-26},
journal = {JMIR Mhealth Uhealth},
volume = {8},
number = {11},
pages = {e21543},
abstract = {Background: Hand tremor typically has a negative impact on a person's ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Objective: Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Methods: Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. Results: We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, $tau$=0.5367379; n=11). An analysis of the ``before'' and ``after'' medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Conclusions: Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Hand tremor typically has a negative impact on a person's ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Objective: Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Methods: Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. Results: We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, $tau$=0.5367379; n=11). An analysis of the ``before'' and ``after'' medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Conclusions: Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.
Inproceedings
[I1]
Subaru Atsumi, Riku Ishioka, Kota Tsubouchi, Yuuki Nishiyama, Kaoru Sezaki
Poster: Towards Estimating UV Index with a Smartphone Utilizing GNSS Signals as a Point Cloud Inproceedings Refereed
In: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, pp. 626–627, Association for Computing Machinery, Tokyo, Japan, 2024, ISBN: 9798400705816.
@inproceedings{10.1145/3643832.3661391,
title = {Poster: Towards Estimating UV Index with a Smartphone Utilizing GNSS Signals as a Point Cloud},
author = {Subaru Atsumi and Riku Ishioka and Kota Tsubouchi and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://doi.org/10.1145/3643832.3661391},
doi = {10.1145/3643832.3661391},
isbn = {9798400705816},
year = {2024},
date = {2024-06-05},
urldate = {2024-06-05},
booktitle = {Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services},
pages = {626–627},
publisher = {Association for Computing Machinery},
address = {Tokyo, Japan},
series = {MOBISYS '24},
abstract = {Monitoring and controlling the exposure of an individual to ultraviolet (UV) radiation is crucial for personal health. The use of the global navigation satellite system (GNSS) signals received by a personal off-the-shelf smartphone has been studied as a novel estimation method. In the existing method, satellites are grouped based on their positions and the signal information is represented by group statistics, leading to a coarse estimation. We propose a new UV index estimation method that directly utilizes satellite-wise information and their spatial relationships with a point-cloud neural network, considering the similarity between GNSS signals and point clouds. We collected GNSS signals and UV index data from two locations within the same area and demonstrated that the proposed method enhances the estimation accuracy and smoothness.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Monitoring and controlling the exposure of an individual to ultraviolet (UV) radiation is crucial for personal health. The use of the global navigation satellite system (GNSS) signals received by a personal off-the-shelf smartphone has been studied as a novel estimation method. In the existing method, satellites are grouped based on their positions and the signal information is represented by group statistics, leading to a coarse estimation. We propose a new UV index estimation method that directly utilizes satellite-wise information and their spatial relationships with a point-cloud neural network, considering the similarity between GNSS signals and point clouds. We collected GNSS signals and UV index data from two locations within the same area and demonstrated that the proposed method enhances the estimation accuracy and smoothness.
[I2]
Riku Ishioka, Yuuki Nishiyama, Kota Tsubouchi, Kaoru Sezaki
Poster abstract: UV index estimation leveraging GNSS sensors on smartphones Inproceedings Refereed
In: Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA, pp. 863–864, Association for Computing Machinery, New York, NY, USA, 2022.
@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}
}
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.
[I3]
Elina Kuosmanen, Valerii Kan, Julio Vega, Aku Visuri, Yuuki Nishiyama, Anind K Dey, Simon Harper, Denzil Ferreira
Challenges of Parkinson’s Disease: User Experiences with STOP Inproceedings
In: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, Association for Computing Machinery, Taipei, Taiwan, 2019, ISBN: 9781450368254.
@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}
}
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.
[I4]
Elina Kuosmanen, Valerii Kan, Aku Visuri, Julio Vega, Yuuki Nishiyama, Anind K Dey, Simon Harper, Denzil Ferreira
Mobile-Based Monitoring of Parkinson’s Disease Inproceedings
In: Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia, pp. 441–448, Association for Computing Machinery, Cairo, Egypt, 2018, ISBN: 9781450365949.
BibTeX | Links:
@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}
}
Domestic Conference
Sorry, no publications matched your criteria.
Book Chapters
Sorry, no publications matched your criteria.