A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for Internet of Things healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym PAs (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary, and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class support vector machine is applied to coarsely classify free weight and nonfree weight activities. In the second layer, a neural network is utilized for aerobic and sedentary activities recognition; a hidden Markov model is to provide a further classification in free weight activities. The performance of the framework was tested on ten healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/m
²
; and body fat: 20.5 ± 5.4), and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be extended in supporting more types of PA recognition in complex applications.