In this paper, we describe Remote Monitoring Validation Engineering System (ReMoVES), a newly-developed platform for motion rehabilitation through serious games and biophysical sensors. The main features of the system are highlighted as follows: motion tracking capabilities through Microsoft Kinect V2 and Leap Motion are disclosed and compared with other solutions; the emotional state of the patient is evaluated with heart rate measurements and electrodermal activity monitored by Microsoft Band 2 during the execution of the functional exercises planned by the therapist. The ReMoVES platform is conceived for home-based rehabilitation after the hospitalisation period, and the system will deploy machine learning techniques to provide an automated evaluation of the patient performance during the training. The algorithms should deliver effective reports to the therapist about the training performance while the patient exercises on their own. The game features that will be described in this manuscript represent the input for the training set, while the feedback provided by the therapist is the output. To face this supervised learning problem, we are describing the most significant features to be used as key indicators of the patient's performance along with the evaluation of their accuracy in discriminating between good or bad patient actions.

No abstract

This paper describes the biomedical, remote monitoring infrastructure developed and currently tested in the EU REHAB@HOME project to support home rehabilitation of the upper extremity of persons post-stroke and in persons with other neurological disorders, such as Multiple Sclerosis patients, in order to track their progress over therapy and improve their Quality of Life. The paper will specifically focus on describing the initial testing of the tele-rehabilitation system's components for patients' biomedical monitoring over therapy, which support the delivery and monitoring of more personalized, engaging plans of care by rehabilitation centers and services.

No abstract

In this paper, we describe the Rehab@Home Operational Infrastructure which functioning essentially relies on the acquisition, processing, exchange and interpretation of a large set of heterogeneous data and information. These data are coming from existing clinical data records, rehabilitation workflow structure, user-system interaction, and explicit user feedback, basic information about expected and actual rehabilitation progress, biophysical sensors, ambient and contextual sensors. What in a more precise and detailed way has been described and analyzed is the specification and development of data protocol and data integration devoted to the acquisition, processing, exchange and interpretation of a large set of heterogeneous data and information coming from biophysical sensors, ambient and contextual sensors, existing clinical data records. It has been carried a study of user profiling and personalization, which will be exploited to adapt process and services with the aim of enhancing user satisfaction. Thanks to personalization of the user-system interaction, the explicit user feedback, the basic information about expected and actual rehabilitation progress are made available in the best way. Case-based reasoning further improves the extraction of useful information from a single patient and from compared analysis. Identification of the most relevant risk factors related to the rehabilitation process and the monitoring of the whole rehabilitation process was another field of study