Cited By
View all- Fahim MSillitti A(2019)Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2019.29219127(81664-81681)Online publication date: 2019
Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients
Pages 305 - 306
Abstract
In this work, we present a platform for continuously monitor and guide stroke patient at home during Activities of the Daily Living. The platform consists of a smart cup which embeds sensors that monitor the patient's hand and arm motor activity at different times of the day. The cup detects its orientation, the liquid level, its position on a specific target, as well as tremors. Moreover, displays are provided to guide the patient's movement. Finally, the planned studies with both the therapists and patients are presented.
References
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M. Anastassova, C. Mégard, and J.-M. Burkhardt. Prototype evaluation and user-needs analysis in the early design of emerging technologies. In Human-computer interaction. Interaction design and usability, pages 383--392. Springer, 2007.
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B. Gialanella, R. Santoro, and C. Ferlucci. Predicting outcome after stroke: the role of basic activities of daily living predicting outcome after stroke. European journal of physical and rehabilitation medicine, 49(5):629--637, 2013.
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J. S. Kim. Delayed onset hand tremor caused by cerebral infarction. Stroke, 23(2):292--294, 1992.
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S. Pandian and K. N. Arya. Stroke-related motor outcome measures: Do they quantify the neurophysiological aspects of upper extremity recovery? Journal of bodywork and movement therapies, 18(3):412--423, 2014.
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R. Stanton, L. Ada, C. M. Dean, and E. Preston. Feedback received while practicing everyday activities during rehabilitation after stroke: An observational study. Physiotherapy Research International, 2014.
Index Terms
- Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients
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Published In
October 2016
362 pages
ISBN:9781450341240
DOI:10.1145/2982142
- General Chair:
- Jinjuan Heidi Feng,
- Program Chair:
- Matt Huenerfauth
Copyright © 2016 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 October 2016
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ASSETS '16
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ASSETS '16: The 18th International ACM SIGACCESS Conference on Computers and Accessibility
October 23 - 26, 2016
Nevada, Reno, USA
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ASSETS '16 Paper Acceptance Rate 24 of 95 submissions, 25%;
Overall Acceptance Rate 436 of 1,556 submissions, 28%
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View all- Fahim MSillitti A(2019)Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2019.29219127(81664-81681)Online publication date: 2019
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