Daily living activity recognition using wearable devices: A features-rich dataset and a novel approach

Automated daily living activity recognition is a relevant task since it allows to assess the health status of a subject both objectively and remotely. Having a reliable measure is important since it gives precise indications to doctors and researchers interested in evaluating the effectiveness of treatments or drugs (e.g., in the context of clinical stud-ies). The possibility to perform this task remotely is more convenient for the patients and acquired increasing importance not only due to the current pandemic, but also because of the regularly growing population of elderly people that could benefit from remote monitoring. In this paper, first, we describe a novel wearable-device-based dataset that contains data (1) of a high number of daily life activities, coming from a real-life scenario, (2) recorded by applying multiple devices on different parts of the body, and (3) recorded with medical-grade devices at a high sampling frequency. Then, second, we describe a machine learning-based method for activity recognition. Our approach takes in input a dataset and through multiple phases allows to recognise the activities performed by the subjects with a good degree of accuracy (up to 0.92 expressed as F1 score depending on the location).