Towards Sensorized Glasses: A Smart Wearable System for Head Movement Monitoring

Monitoring patient movements is crucial in various medical applications, including, among others, disease diagnosis, health monitoring, activity detection, and remote rehabilitation. This study is aimed at determining patient balance and potentially estimate the risk of fall by using inertial data collected by a pair of smart glasses equipped with a single IMU. To this purpose, head motion signals and the impact of body movements on their identification to extract high level information based on the analysis of Statistical Features (SF), Dynamic Time Warping (DTW) and Machine Learning (ML) applied to the multivariate time series of signals collected by the wearable sensing node has been considered. Experimental results show good performance in terms of classification accuracy in identifying a set of head movements, making this preliminary study a promising solution for the development of smart glasses for remote patient monitoring,