Design of a Kinematic and Emotional Assessment Module for the Tele-rehabilitation Platform

Rybarczyk, Yves; Leconte, Louis; Pérez-Medina, Jorge Luis; Vargas, Karina Jimenes; Vargas, Patricia Acosta-; Esparza, Danilo

doi:10.7763/ijmo.2019.v9.692

Cited by 3 publications

(2 citation statements)

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“…The application of telerehabilitation is an expanding option in various health scenarios. Nevertheless, there are some limitations that must be taken into account [32]. In particular, the current COVID-19 pandemic has revealed the need and opportunity for telemedicine in future healthcare systems.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

et al. 2023

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The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.

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Section: Related Workmentioning

confidence: 99%

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

et al. 2023

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“…Funding provided by NSF award 0339122. http://wiki.cnbc.cmu.edu/Face_Place detection with the Haarcascades code, and predicts the emotion of the detected face. Preliminary results on a reduced number of participants show that the emotion recognition algorithm is able to discriminate pain from no pain with 100% accuracy [42]. Figure 18 shows a representation of an individual performing an exercise of Hip abduction.…”

Section: Four Types Of Exercises Are Currently Available: ''Hip Abducmentioning

confidence: 99%

ePHoRt: Towards a Reference Architecture for Tele-Rehabilitation Systems

et al. 2019

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In recent years, the software applications for medical assistance, including the telerehabilitation, have known a high and a continuous presence in the medical area. The ePHoRt is a Web-based platform for the remote home monitoring rehabilitation exercises in patients after hip replacement surgery. It involves a learning phase and a serious game scheme for the execution and evaluation of the exercises as part of a therapeutic program. Modular software architecture is proposed, under the patient perspective, to be used as a reference model for researchers or professionals who wish to carry out tele-rehabilitation platforms, and to guarantee security, flexibility, and scalability. The architecture incorporates two main components. The first one manages the patient' therapeutic programs taking into account two principles: 1) maintain loose coupling between the layers of the framework and 2) Don't Repeat Yourself (DRY). The second one evaluates the performed exercises in real time considering an independent acquisition mechanism for the patient movements and two artificial algorithms. The first algorithm allows evaluating the quality of the movements, while the second one allows assessing the levels of pain intensity by recognizing the patient' emotions when performing the movements. Details of the components and the meta-model of the architecture are presented and discussed considering their advantages and disadvantages.

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