A Novel CNN Architecture for Accurate Recognition of Elastic Band Training Poses

Suresh R; Kumar V; Jeyaganesan A; Thangaraj P; Athisayaraj S; Viswanath Sundar; Ramakrishnan R

doi:10.54392/irjmt2534

Authors

Suresh R Department of Physical Education, Hindustan College of Arts & Science (Affiliated to University of Madras) Padur, Chennai-603103, Tamil Nadu, India. Author
Kumar V Department of Physical Education, St. Xavier's College, Palayamkottai, Tamil Nadu, India Author
Jeyaganesan A Department of Physical Education, Guru Nanak College (Autonomous), Affiliated to University of madras, Chennai, Tamil Nadu, India Author
Thangaraj P Department of Physical Education, St. Xavier's College, Palayamkottai, Tamil Nadu, India Author
Athisayaraj S Department of Physical Education, St. Xavier's College, Palayamkottai, Tamil Nadu, India Author
Viswanath Sundar Department of Physical Education and Sports Science, Visva-Bharati – A Central University and an Institution of National Importance of Santiniketan, West Bengal, India. Author
Ramakrishnan R Department of Physical Education and Sports Sciences, Hindustan Institute of Technology and Science, Padur, Chennai-603103, Tamil Nadu, India. Author

DOI:

https://doi.org/10.54392/irjmt2534

Keywords:

Band Exercise, Biceps Curl, Chest Press, Leg Press & Convolutional Neural Networks (CNN)

Abstract

Band practice resistance is a popular form of exercise that uses elastic bands or pipes to resist different activities. Exercises such as bicep curls, breast presses and leg presses can effectively attach the muscles by adjusting the range to different movement patterns and movement when appearing with resistance tape. In addition, ties provide adjustable resistance, where the resistance increases because the tape is long, and offers a specific challenge in complete activity. Band practices provide an accessible and effective method for improving the power, stability and improvement of muscle equipment, used in rehabilitation, prevention of injuries or used in general fitness. The purpose of research is to build an intensive teaching model specially designed to recognize and classify training positions for the band. Research has appointed a skilled intensive teaching architectonic neural network (CNN) that is suitable for image classification applications. The model is learned to identify the unique characteristics of each training position and allow accurate classification. If the situation is considered incorrect, the system provides real -time response to the user and recommends changes to increase the form and reduce damage.

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