Enhancing inertial sensor-based sports activity recognition through reduction of the signals and deep learning

The increasing demand for sport training and health monitoring aligns with contemporary lifestyle trends. Developing a system to support sport training and verify exercise correctness can significantly enhance the acquisition of detailed, subject-specific data. This study aims to evaluate the accuracy of sport exercise recognition while minimizing the number of sensors required. The dataset includes 8,968 samples of exercises such as bar dips, squats, dips, lunges, pull-ups, sit-ups, and push-ups. Data were collected from 60 signals using mobile sensors positioned at the chest, right hand, and right foot of 21 subjects. The objective is to identify the most efficient method for recognizing these activities. Our methodology involved experiments with 21 participants in a custom-built setup and the application of deep learning techniques. Initially, a novel algorithm identified the optimal set of signals. We then tested two scenarios: training a Convolutional Neural Network (CNN) on raw signals and on pre-processed signals to reduce noise. The findings indicate that the magnetic field (MF) signal is crucial for recognizing exercises in both filtered and unfiltered data sets. The CNN’s accuracy was 2–3% higher with unfiltered data and remained robust at 93.7% for training and 90.0% for testing, despite a reduction in model complexity. This method’s practical implications are significant, enhancing sports training systems by reducing the number of sensors needed, thereby improving user comfort. Additionally, it contributes valuable insights to the field of Human Activity Recognition.