Research Article
Activity Level Assessment of Wheelchair Users Using Smart Cushion
@INPROCEEDINGS{10.4108/eai.15-12-2016.2267668, author={Congcong Ma and Raffaele Gravina and Wenfeng Li and Yu Zhang and Qimeng Li and Giancarlo Fortino}, title={Activity Level Assessment of Wheelchair Users Using Smart Cushion}, proceedings={11th International Conference on Body Area Networks}, publisher={ACM}, proceedings_a={BODYNETS}, year={2017}, month={4}, keywords={activity level assessment smart cushion wheelchair users posture sequences hamming weight}, doi={10.4108/eai.15-12-2016.2267668} }- Congcong Ma
Raffaele Gravina
Wenfeng Li
Yu Zhang
Qimeng Li
Giancarlo Fortino
Year: 2017
Activity Level Assessment of Wheelchair Users Using Smart Cushion
BODYNETS
EAI
DOI: 10.4108/eai.15-12-2016.2267668
Abstract
A large majority of worldwide population, such as office workers and long journey vehicle drivers, spends lot of time everyday in sedentary activities. In this paper, we specifically focus on the assessment of different levels of wheelchair users' activity. The postures that wheelchair users assume during daily activities hide valuable information that can reveal their wellness and general health condition.
Aiming at mining such underlying information, we propose a hamming weight based algorithm to assess the activity level from sitting posture sequences. Using a smart cushion placed on the wheelchair, we can monitor users' postures. Postures sequence is transformed into binary value vector segments in order to reduce the computation load. The algorithm can detect three levels of activity (stationary, moderate or hyperactivity) with high accuracy. The proposed method could be embedded into several potential applications such as health estimation of sitting subjects, activity statistics, and detection of abnormal activities.