Neural Network-based Wearable Devices for Limb Rehabilitation by Inertial Signal Classification

Recent progress has led to the widespread use of Machine Learning (ML) in various fields, including biomedical applications. ML can assist in the diagnosis by detecting disease-specific patterns and monitoring the patient's condition. Furthermore, ML algorithms can be implemented on resource-limited systems thanks to emerging technologies like the Internet of Things (IoT), Edge Computing, and wearable devices, enabling real-time disease detection and faster processing. This research explores wearable Neural Network (NN) applications using inertial signals. A multi-purpose wearable device has been developed for arm gesture recognition, fall detection, stair climbing and descending recognition, and gait and physical activity level monitoring. NN-based classifiers have been trained on the Edge Impulse platform and implemented on nRF52840 microcontroller. These classifiers achieved high accuracy (from 95.74% up to 100%), using a small memory portion (from 4.4kB to 9.4 kB of RAM and from 17.4kB to 99.8 kB of Flash memory).