Journals
[J1]
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]
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.
[I2]
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.
[I3]
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}
}
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