Reference : Home Self-Training: Visual Feedback for Assisting Physical Activity for Stroke Survivors

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/39532

Title :	Home Self-Training: Visual Feedback for Assisting Physical Activity for Stroke Survivors
Language :	English
Author, co-author :	Baptista, Renato [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ghorbel, Enjie [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Shabayek, Abd El Rahman [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Moissenet, Florent [Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d‘Analyse du Mouvement et de la Posture (LAMP), Luxembourg]
	Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Douchet, Alice [Fondation Hopale, France]
	André, Mathilde [Fondation Hopale, France]
	Pager, Julien [Fondation Hopale, France]
	Bouilland, Stéphane [Fondation Hopale, France]
Publication date :	2019
Journal title :	Computer Methods and Programs in Biomedicine
Publisher :	Elsevier
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	0169-2607
City :	Limerick
Country :	Netherlands
Keywords :	[en] Stroke-survivors ; Home-based rehabilitation ; Visual feedback ; 3D skeleton
Abstract :	[en] Background and Objective: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist. Methods: A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion. Results: The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback. Conclusions: Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	European Commission - EC
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/39532
DOI :	10.1016/j.cmpb.2019.04.019
Framework Programme / European Project :	H2020 ; 689947 - STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs

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