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BIGHand - A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise: Demo abstract

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
EditorsMi Zhang
PublisherAssociation for Computing Machinery
Pages388-389
Number of pages2
ISBN (Electronic)9781450369503
DOIs
StatePublished - Nov 10 2019
Event17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019 - New York, United States
Duration: Nov 10 2019Nov 13 2019

Publication series

NameSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems

Conference

Conference17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019
Country/TerritoryUnited States
CityNew York
Period11/10/1911/13/19

Keywords

  • Exergame
  • Rehabilitation
  • Sensor system
  • Smart health
  • Stroke

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