TY - GEN
T1 - BIGHand - A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise
T2 - 17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019
AU - Comstock, Emery
AU - Guo, Gabriel
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2019 Authors.
PY - 2019/11/10
Y1 - 2019/11/10
N2 - Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including "smart" post-game feedback and in-game goals. A demo video is available at https://youtu.be/zrLVkZZ4Ukc.
AB - Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including "smart" post-game feedback and in-game goals. A demo video is available at https://youtu.be/zrLVkZZ4Ukc.
KW - Exergame
KW - Rehabilitation
KW - Sensor system
KW - Smart health
KW - Stroke
UR - https://www.scopus.com/pages/publications/85076589917
U2 - 10.1145/3356250.3361949
DO - 10.1145/3356250.3361949
M3 - Conference contribution
AN - SCOPUS:85076589917
T3 - SenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
SP - 388
EP - 389
BT - SenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
A2 - Zhang, Mi
PB - Association for Computing Machinery
Y2 - 10 November 2019 through 13 November 2019
ER -