Exercise Classification with Machine Learning

Abstract: Innowearable AB has developed a product called Inno-XTM that calculates musclefatigue during three exercises: squat jumps, wall sit, and leg extension. Inno-X uses an accelerometer and a surface electromyography sensor. The goal of thisproject was to create the signal processing part of a machine-learning (ML) pipeline that classifies the exercises in real-time. Data was collected from the sensors to create a training environment that could later be translated to a real-time environment using a sliding window technique. A Savitsky-Golay filter (SG), lowpass, and highpass filters were tested in order to remove noise from the signal. The best filter proved to be the SG filter. Both time and frequency domain features were used in feature extraction. The finished product used 24 features from both domains combined. These methods together with the ML algorithms created in a collabora-tive project led to a classification accuracy for the training environment of 98.62%, while the real-time environment reached 90%. By collecting a larger and more diverse dataset, and addressing the issue of leg extension and wall sit exercises being too similar, real-time classification can be further improved which will make the ML pipeline usable for Innowearables’ customers.