Enabling Automatic Monitoring of Fluid Intake and Medical Adherence by Human Activities Recognition from a Wrist-Mounted MIMU Sensor.

The automatic identification of human activities is an important application for enabling remote home monitoring of health status in elderly and fragile people. Nowadays, commercially available wearable devices found a widespread diffusion, embedding various types of sensors that can be used for correctly recognizing activities of daily living (ADL). This work deals with the identification of ADL based on upper limb gestures from a single magnetic inertial measurement unit (MIMU) placed on the wrist. In particular, emphasis was devoted to the identification of drinking and pill intake, being direct indicators of water and medication assumption. To this aim, machine learning (ML) and deep learning (DL) models were directly compared, and single MIMU recordings were evaluated on three classification experiments. Outcomes showed that DL pipeline outperformed ML in distinguishing 14 ADL, with the best accuracy provided by the combination of gyroscope and magnetometer data (about 93%). The same configuration reached over 97% accuracy in identify drinking and pill intake among the other 12 confounding ADL. This study is a step toward the development of a robust solution for the continuous and minimally invasive monitoring of hydration behaviors and adherence to medical prescription within the home environment.Clinical relevance- This work provides evidences about the feasibility of a continuous and non-invasive monitoring of the hydration behaviors and adherence to medical prescriptions in elderly individuals.