Predicting physiological activity using sensor networks

In Canada, 35% of injuries occur during participation in sports or exercise (Billette, 2011). Sports-related injuries costs the Canadian healthcare system $1.5 billion annually (Parachute, 2015). Injury prevention in sports is of paramount to the athlete, coaches, team management, and health team. Overtraining and fatigue are common causes for injury in sports. Training load is an important factor to optimize athlete performance and minimize risk of injury. While accelerations measured with sensors attached to the chest have been used to calculate training load, this approach does not specifically capture motion of the lower body, so cannot adequately capture training load in sporting activities with significant arm motions (such as swimming) or predominant leg motions (such as cycling). The objective of this research project is to develop an algorithm for predicting training load that can be used in a variety of sports.

Faculty Supervisor:

James Dickey

Student:

Jeff Brooks

Partner:

GForce Tracker Inc

Discipline:

Kinesiology

Sector:

Other

University:

Western University