Executive Summary : | Calf muscles play a crucial role in accelerating and decelerating the body, with injuries being more common in athletics, football, badminton, and basketball. Fatigue conditions due to overtraining and prolonged exertion are a major risk factor for calf injury, with up to four players per team per season suffering from this injury in football. This neuromuscular condition reduces muscle force generating capacity and is often observed in activities related to sustained isometric and continuous flexion and extension movements. Measuring calf muscle fatigue helps sports scientists and coaches identify when a person is at risk of injury or overtraining, as well as help athletes identify power, speed, and endurance in their training regimen and make adjustments to improve overall performance. Existing systems for measuring calf muscle fatigue face challenges in accurately capturing and understanding complex physiological factors, providing real-time feedback, and being real-time use in sports environments. This research aims to investigate calf muscle fatigue using a multimodal approach that combines data-driven, mathematical, and physics-informed machine learning (PIML) models. The study will monitor the dynamics of calf muscles using myoelectric activities and kinematic data during isometric and dynamic contractions, analyze these parameters using advanced signal processing approaches, develop force prediction models using surface electromyography and kinematic models, and formulate PIML models to continuously monitor fatigue conditions in calf muscles.
This research directly handles the responsibility of the Department of Sports and Indian Ministry of Youth Affairs and Sports to promote capacity building for excellence in sports at national and international levels. The research will create new scientific knowledge base that can be used to design training programs to improve endurance and reduce injury risks in athletes, coaches, and sportspeople. |