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In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : E-health is a growing research topic, especially with the expansion of the Internet of Things (IoT). Miniaturized wearable sensors are auspicious tools for biomedicine and healthcare systems. In this paper, we present D-SORM, a sensor fusion-based digital solution intended to assist clinicians and improve their diagnosis by providing objective measurements and automatic recognition. The aim is to supply an interface for remote monitoring to the medical staff.

METHODS : D-SORM platform estimates the wearable device attitude based on its acquired data, and visualizes it in real-time using a graphical user interface (GUI). It also integrates two modules which serve two different medical applications. The first one is arm tele-rehabilitation, where sessions are done online. The practitioner gives the instructions while wearing the device, and the patient has to reproduce the gestures. A processing unit is dedicated to compute statistical features and calculate the success rate. The second one is human motion tracking for elderly care. A novel machine learning architecture is proposed, based on feature fusion, to predict the activities of daily living.

RESULTS : The rehabilitation mechanism was tested under supervised conditions, by performing a set of movements. D-SORM provides extra information and objective measurements, thus facilitates the diagnosis of clinicians. The human activity recognition is also validated using a public dataset. With D-SORM, an efficiency ranging from 97.7% to 99.65% is ensured under unsupervised conditions.

CONCLUSIONS : The proposed design constitutes a digital clinical tool for medical teams allowing remote health monitoring. It overcomes geographical barriers while providing faster and highly accurate assessment.

Abbas Manuel, Somme Dominique, Le Bouquin Jeannès Régine


Activity recognition, Arm tele-rehabilitation, Device attitude, E-health, Machine learning, Sensor fusion