Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Riku Ishioka, Yuuki Nishiyama, Kota Tsubouchi, Kaoru Sezaki
Poster abstract: UV index estimation leveraging GNSS sensors on smartphones Inproceedings
In: Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA, Association for Computing Machinery, New York, NY, USA, 2022.
BibTeX | タグ: GNSS, Passive mobile sensing, smartphone, UV index estimation
@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},
year = {2022},
date = {2022-11-06},
urldate = {2022-11-06},
booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {SenSys '22},
keywords = {GNSS, Passive mobile sensing, smartphone, UV index estimation},
pubstate = {published},
tppubtype = {inproceedings}
}
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 | タグ: 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}
}
Zengyi Han
Context Aware Photo Protection for In-SituSharing Inproceedings
In: MobileHCI '20: Proceedings of the 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Doctoral Consortium), Virtual Event, Germany, 2020.
Abstract | BibTeX | タグ: Context-Aware, Face Recognition, In-situ Photo Sharing, smartphone
@inproceedings{han2020_mobilehci,
title = {Context Aware Photo Protection for In-SituSharing},
author = {Zengyi Han},
year = {2020},
date = {2020-10-05},
booktitle = {MobileHCI '20: Proceedings of the 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Doctoral Consortium), Virtual Event, Germany},
abstract = {People nowadays are getting used to using mobile phones for daily photography, and sharing these precious moments online or in-situ with their friends. However, there is a potential risk of privacy leakage during the in-situ photo-sharing process. To address this risk, we proposeOASIS, a cOntext Aware photoprotection for in-SItuSharing behavior: using the front camera to tell different viewers, OASIScustomize viewer’s photo gallery seamlessly between different viewers according to the context of the photo. In this way, we can provide viewers with a good sharing experience while protecting the privacy of the owner. The paper is presented at MobileHCI 2020 Doctoral Consortium.},
keywords = {Context-Aware, Face Recognition, In-situ Photo Sharing, smartphone},
pubstate = {published},
tppubtype = {inproceedings}
}
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 | タグ: empirical evaluation, logging, Parkinson's disease, smartphone | Links:
@inproceedings{10.1145/3338286.3340133b,
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 = {2019-10-05},
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}
}
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 | タグ: 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}
}
Satoshi Hyuga, Masaki Ito, Masayuki Iwai, Kaoru Sezaki
Estimate a User’s Location Using Smartphone’s Barometer on a Subway Inproceedings
In: Proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT '15) , pp. 1-4, Association for Computing Machinery, New York, NY, USA, 2015, ISBN: 9781450339681.
Abstract | BibTeX | タグ: barometer, location estimation, smartphone, subway | Links:
@inproceedings{inproceedingsi,
title = {Estimate a User’s Location Using Smartphone’s Barometer on a Subway},
author = {Satoshi Hyuga and Masaki Ito and Masayuki Iwai and Kaoru Sezaki},
doi = {10.1145/2830571.2830576},
isbn = {9781450339681},
year = {2015},
date = {2015-11-01},
booktitle = {Proceedings of the 5th International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments (MELT '15) },
pages = {1-4},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {MELT ’15},
abstract = {Knowing the location of a train is necessary to develop a useful service for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate especially on a subway. As an alternative method, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses only a barometer on a smartphone. We estimate motion state from the change of elevation, and also estimate latest stop station by the similarity of a series of elevations recorded when the train stopped and actual elevations of stations. By estimation of the motion state and the latest stop station, development of various context-aware services for subway passengers becomes possible. Through experiments in four lines of subway in Tokyo, we demonstrated that the accuracy of estimation of the motion state is 86%, and estimation of the stop station is 58%.},
keywords = {barometer, location estimation, smartphone, subway},
pubstate = {published},
tppubtype = {inproceedings}
}