Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60064Related data: https://doi.org/10.21950/7YRNMV
DOI: 10.1016/j.eswa.2022.116990
ISSN: 0957-4174
Date
2022-08-01Funders
Spanish Ministry of Science, In-novation and Universities
Bibliographic citation
Expert Systems with Applications, 2022, v. 199, n. , p.1 - 15
Keywords
Motion recognition
Performance evaluation
Inertial measurement units
IMU
Machine learning
ML
Virtual coach
Elderly
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095168-B-C51/ES/SISTEMAS DE POSICIONAMIENTO LOCAL: ENFOQUE HOLISTICO DESDE LAS TECNOLOGIAS BASE HASTA LAS APLICACIONES/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
© 2022 The Authors
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
Home-based physical therapies are effective if the prescribed exercises are correctly executed and patients adhere to these routines. This is specially important for older adults who can easily forget the guidelines from therapists. Inertial Measurement Units (IMUs) are commonly used for tracking exercise execution giving information of patients' motion data. In this work, we propose the use of Machine Learning techniques to recognize which exercise is being carried out and to assess if the recognized exercise is properly executed by using data from four IMUs placed on the person limbs. To the best of our knowledge, both tasks have never been addressed together as a unique complex task before. However, their combination is needed for the complete characterization of the performance of physical therapies. We evaluate the performance of six machine learning classifiers in three contexts: recognition and evaluation in a single classifier, recognition of correct exercises, excluding the wrongly performed exercises, and a two-stage approach that first recognizes the exercise and then evaluates it. We apply our proposal to a set of exercises of the upper-and lower-limbs designed for maintaining elderly people health status. To do so, the motion of 30volunteers were monitored with 4 IMUs. We obtain accuracies of 88.4% and the 91.4% in the two initial scenarios. In the third one, the recognition provides an accuracy of 96.2%, whereas the exercise evaluation varies between 93.6% and 100%. This work proves the feasibility of IMUs for a complete monitoring of physical therapies in which we can get information of which exercise is being performed and its quality, as a basis for designing virtual coaches.
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Simultaneous_GarciaVilla_2022.pdf | 1.699Mb |
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Simultaneous_GarciaVilla_2022.pdf | 1.699Mb |
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