Characterization of a wearable system for automatic supervision of fitness exercises

It is widely known that physical activity is an effective tool for preventing several diseases. However, unsupervised training may lead to poor execution quality, resulting in ineffective training, or even injuries in worst cases. Automatic tracking and quantification of exercise efforts by means of wearables could be a way to monitor the execution correctness. As a positive side effect, these devices help in motivating people, increasing the quantity of physical exercises of users and thus improving health conditions as well. Unfortunately, despite the availability of some commercial devices, their performance and effectiveness are not documented. This work proposes a new solution that exploits machine learning (ML) techniques (in particular Linear Discriminant Analysis, LDA) for analyzing data coming from wearable Inertial Measurement Units (IMUs). Efforts have been done in reducing the computational requirements, in order to be compatible with constraints in perspective of embedded implementation. The experimental campaign carried out to measure the performance showed an average accuracy, recall and precision on the order of 97%, 93% and 90%, respectively.