Cited By
View all- Komukai KOhmura R(2019)Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation RecordingHuman Activity Sensing10.1007/978-3-030-13001-5_1(3-15)Online publication date: 10-Sep-2019
Exploring the Number and Suitable Positions of Wearable Sensors in Automatic Rehabilitation Recording
Authors: 
Kohei Komukai, Ren OhmuraAuthors Info & Claims
Kohei Komukai, Ren OhmuraAuthors Info & ClaimsPages 1414 - 1423
Abstract
Since taking a detailed record of rehabilitation is difficult due to the busyness of practitioners, the chance of making quantitative analysis of rehabilitation is degraded. Automatic rehabilitation recording by activity recognition using wearable sensors offers the dual prospect of decreasing a practitioners' load and enabling quantitative analysis of rehabilitation. For highly accurate activity recognition, it is desirable to use a large number of sensors. On the other hand, usage of a larger number of wearable sensors increases a patients' discomfort and decreases practicality. Thus, we investigate the suitable number and positions of wearable sensors for activity recognition in rehabilitations. Experiments were carried out with 16 healthy people as subjects for 10 different behaviors in rehabilitation performed with seven inertial measurement units (IMUs) on the subjects' body. Then, we examined recognition accuracy while reducing the number of sensors with all combinations of sensor positions. As a result, 0.833 of F-measure value can be obtained with three sensors at the waist, right thigh and right lower leg.
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- Exploring the Number and Suitable Positions of Wearable Sensors in Automatic Rehabilitation Recording
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October 2018
1881 pages
ISBN:9781450359665
DOI:10.1145/3267305
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Published: 08 October 2018
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UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing
October 8 - 12, 2018
Singapore, Singapore
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View all- Komukai KOhmura R(2019)Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation RecordingHuman Activity Sensing10.1007/978-3-030-13001-5_1(3-15)Online publication date: 10-Sep-2019
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