Title | ||
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Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors |
Abstract | ||
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Maximizing the motor practice in stroke survivors' living environments may significantly improve the functional recovery of their stroke-affected upper-limb. A wearable system that can continuously monitor upper-limb performance has been considered as an effective clinical solution for its potential to provide patient-centered, data-driven feedback to improve the motor dosage. Towards that end, we investigate a system leveraging a pair of finger-worn, ring-type accelerometers capable of monitoring both gross-arm and fine-hand movements that are clinically relevant to the performance of daily activities. In this work, we conduct a mixed-methods study to (1) quantitatively evaluate the efficacy of finger-worn accelerometers in measuring clinically relevant information regarding stroke survivors' upper-limb performance, and (2) qualitatively investigate design requirements for the self-monitoring system, based on data collected from 25 stroke survivors and seven occupational therapists. Our quantitative findings demonstrate strong face and convergent validity of the finger-worn accelerometers, and its responsiveness to changes in motor behavior. Our qualitative findings provide a detailed account of the current rehabilitation process while highlighting several challenges that therapists and stroke survivors face. This study offers promising directions for the design of a self-monitoring system that can encourage the affected limb use during stroke survivors' daily living.
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Year | DOI | Venue |
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2019 | 10.1145/3369817 | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies |
Keywords | Field | DocType |
Ring accelerometers,finger-worn accelerometers,hemiparesis,mixed-methods study,self-monitoring,stroke rehabilitation,stroke survivor,upper limb | Stroke,mHealth,Physical medicine and rehabilitation,Medicine,Motor function | Journal |
Volume | Issue | Citations |
3 | 4 | 1 |
PageRank | References | Authors |
0.35 | 17 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yoojung Kim | 1 | 24 | 5.15 |
Hee-tae Jung | 2 | 34 | 10.22 |
Joonwoo Park | 3 | 1 | 0.35 |
Yangsoo Kim | 4 | 2 | 3.83 |
Nathan Ramasarma | 5 | 4 | 1.18 |
p bonato | 6 | 282 | 39.26 |
Eun Kyoung Choe | 7 | 518 | 38.00 |
Sunghoon Ivan Lee | 8 | 65 | 16.80 |