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A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction
Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512175/ https://www.ncbi.nlm.nih.gov/pubmed/34641012 http://dx.doi.org/10.3390/s21196692 |
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author | Hannan, Abdul Shafiq, Muhammad Zohaib Hussain, Faisal Pires, Ivan Miguel |
author_facet | Hannan, Abdul Shafiq, Muhammad Zohaib Hussain, Faisal Pires, Ivan Miguel |
author_sort | Hannan, Abdul |
collection | PubMed |
description | Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature. |
format | Online Article Text |
id | pubmed-8512175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85121752021-10-14 A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction Hannan, Abdul Shafiq, Muhammad Zohaib Hussain, Faisal Pires, Ivan Miguel Sensors (Basel) Article Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature. MDPI 2021-10-08 /pmc/articles/PMC8512175/ /pubmed/34641012 http://dx.doi.org/10.3390/s21196692 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hannan, Abdul Shafiq, Muhammad Zohaib Hussain, Faisal Pires, Ivan Miguel A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title | A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title_full | A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title_fullStr | A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title_full_unstemmed | A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title_short | A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction |
title_sort | portable smart fitness suite for real-time exercise monitoring and posture correction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512175/ https://www.ncbi.nlm.nih.gov/pubmed/34641012 http://dx.doi.org/10.3390/s21196692 |
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