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ThinkMind // GLOBAL HEALTH 2019, The Eighth International Conference on Global Health Challenges // View article global_health_2019_2_30_70072


HemiPhysio App

Authors:
Moez ur Rehman
Tauseef Kamal

Keywords: Smartwatch; Hemiparesis; machine learning; alerts and haptic feedback; activity and gesture detection

Abstract:
A smartwatch’s constant contact with the wrist provides opportunity to measure the differences in the movements of hands (upper limbs) in children with Hemiparetic Cerebral Palsy (CP) while performing daily physical activity (e.g., walking, eating). Children with Hemiparesis have significant weakness (spastic, contractures) on one side of the body that leads to impaired functions on that side. This paper gives the concept and design of a “HemiPhysio” app to collect the activity-related data of both upper limbs using sensors in a smartwatch. The machine learning models trained on collected data are then used to detect the impaired functions by the smartwatch. The child is then instructed for appropriate movement or correcting posture using alert or haptics. The HemiPhysio app should work for dynamic motion activities (e.g., walking) and for the static posture activities (e.g., eating), as well as the related upper limbs movements in these activities. The goal of our application is to encourage Hemiparetic children to use both hands during daily activities.

Pages: 32 to 35

Copyright: Copyright (c) IARIA, 2019

Publication date: September 22, 2019

Published in: conference

ISSN: 2308-4553

ISBN: 978-1-61208-742-9

Location: Porto, Portugal

Dates: from September 22, 2019 to September 26, 2019

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