Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications
| dc.contributor.author | Sarker, Anik | en |
| dc.contributor.author | Emenonye, Don-Roberts | en |
| dc.contributor.author | Kelliher, Aisling | en |
| dc.contributor.author | Rikakis, Thanassis | en |
| dc.contributor.author | Buehrer, R. Michael | en |
| dc.contributor.author | Asbeck, Alan T. | en |
| dc.date.accessioned | 2022-03-28T14:02:26Z | en |
| dc.date.available | 2022-03-28T14:02:26Z | en |
| dc.date.issued | 2022-03-16 | en |
| dc.date.updated | 2022-03-24T14:46:35Z | en |
| dc.description.abstract | For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person’s upper body kinematics could give information about which arm motions or movement features are in need of additional therapy, and their location within the home could give context to these motions. To that end, we present a new algorithm for identifying a person’s location in a region of interest based on a Bluetooth received signal strength (RSS) and present an experimental evaluation of this and a different Bluetooth RSS-based localization algorithm via fingerprinting. We further present algorithms for and experimental results of inferring the complete upper body kinematics based on three standalone inertial measurement unit (IMU) sensors mounted on the wrists and pelvis. Our experimental results for localization find the target location with a mean square error of 1.78 m. Our kinematics reconstruction algorithms gave lower errors with the pelvis sensor mounted on the person’s back and with individual calibrations for each test. With three standalone IMUs, the mean angular error for all of the upper body segment orientations was close to 21 degrees, and the estimated elbow and shoulder angles had mean errors of less than 4 degrees. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Sarker, A.; Emenonye, D.-R.; Kelliher, A.; Rikakis, T.; Buehrer, R.M.; Asbeck, A.T. Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications. Sensors 2022, 22, 2300. | en |
| dc.identifier.doi | https://doi.org/10.3390/s22062300 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/109454 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | kinematics | en |
| dc.subject | inertial sensors | en |
| dc.subject | self-supervised learning | en |
| dc.subject | sparse sensors | en |
| dc.subject | activity recognition | en |
| dc.subject | human pose estimation | en |
| dc.subject | localization | en |
| dc.subject | proximity reporting | en |
| dc.subject | Bluetooth beacon | en |
| dc.subject | Bluetooth RSS | en |
| dc.title | Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications | en |
| dc.title.serial | Sensors | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |