Your browser doesn't support javascript.
A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction.
Hannan, Abdul; Shafiq, Muhammad Zohaib; Hussain, Faisal; Pires, Ivan Miguel.
  • Hannan A; Knowledge Unit of System and Technology, University of Management and Technology, Sialkot 51310, Pakistan.
  • Shafiq MZ; Department of Computer Science and Engineering, Università di Bologna, 40126 Bologna, Italy.
  • Hussain F; Al-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore 54890, Pakistan.
  • Pires IM; Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.
Sensors (Basel) ; 21(19)2021 Oct 08.
Article in English | MEDLINE | ID: covidwho-1463798
ABSTRACT
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21196692

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Prognostic study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21196692