Publication

2020
1.	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. Abstract \| BibTeX \| Tags: hand tremor, mobile health, Parkinson disease, smartphone \| Links: @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 = {hand tremor, mobile health, Parkinson disease, smartphone}, pubstate = {published}, tppubtype = {article} } Close 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. Close
2019
2.	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. Abstract \| BibTeX \| Tags: empirical evaluation, logging, Parkinson’s disease, smartphone \| Links: @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 = {empirical evaluation, logging, Parkinson’s disease, smartphone}, pubstate = {published}, tppubtype = {inproceedings} } Close 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. Close
2018
3.	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 \| Tags: empirical evaluation, gamification, logging, Parkinson’s disease, smartphone \| 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 = {empirical evaluation, gamification, logging, Parkinson’s disease, smartphone}, pubstate = {published}, tppubtype = {inproceedings} } Close