TY - GEN
T1 - VRInsole
T2 - 15th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
AU - Oagaz, Hawkar
AU - Sable, Anurag
AU - Choi, Min Hyung
AU - Xu, Wenyao
AU - Lin, Feng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/2
Y1 - 2018/4/2
N2 - Stroke is a leading cause of long-term impairment, causing a fatality if not act upon in time. Home-based post-stroke rehabilitation plays an important role in helping patients to regain normal mobility and functionality at their residence. However, existing home-based rehabilitation approaches fail to effectively motivate patients on frequent engagement with exercise to achieve the intended outcome. In this paper, we develop VRInsole, a synthetical solution combining a Smart Insole footwear sensor and virtual reality (VR), targeting lower extremity mobility training in an immersive environment for stroke rehabilitation. Specifically, the motion information collected from the Smart Insole serve as the input for the VR to perform corresponding exercise animations. To prove the feasibility of VRInsole, an experiment is conducted on the recognition of lower extremity motion direction, which achieves an average accuracy of 93.9%.
AB - Stroke is a leading cause of long-term impairment, causing a fatality if not act upon in time. Home-based post-stroke rehabilitation plays an important role in helping patients to regain normal mobility and functionality at their residence. However, existing home-based rehabilitation approaches fail to effectively motivate patients on frequent engagement with exercise to achieve the intended outcome. In this paper, we develop VRInsole, a synthetical solution combining a Smart Insole footwear sensor and virtual reality (VR), targeting lower extremity mobility training in an immersive environment for stroke rehabilitation. Specifically, the motion information collected from the Smart Insole serve as the input for the VR to perform corresponding exercise animations. To prove the feasibility of VRInsole, an experiment is conducted on the recognition of lower extremity motion direction, which achieves an average accuracy of 93.9%.
UR - https://www.scopus.com/pages/publications/85049687526
U2 - 10.1109/BSN.2018.8329645
DO - 10.1109/BSN.2018.8329645
M3 - Conference contribution
AN - SCOPUS:85049687526
T3 - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
SP - 5
EP - 8
BT - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 March 2018 through 7 March 2018
ER -