Abstract
The analysis and assessment of motor tasks, such as gait, can provide important information on the progress of neurological disorders such as Parkinson's disease (PD). In this paper, we design a Boby Sensor Network (BSN)-based system for the characterization of gait in Parkinsonians through the extraction of kinematic features, in both time and frequency domains, embedding information on the status of the PD. The gait features extraction is performed on a set of 34 PD patients using a BSN formed by only three inertial nodes (one on the chest and one per thigh). We investigate also the relationship between the selected kinematic features and the Unified Parkinson's Disease Rating Scale (UPDRS) scores assigned to patients by expert neurologists. This work extends a previously proposed approach to the analysis of leg agility and sit-to-stand tasks and, as such, represents a further step to develop a system for automatic and comprehensive evaluation of different PD motor tasks. A performance analysis of different classification techniques is carried out, showing the feasibility of an automatic (and, eventually, remote) UPDRS scoring system, suitable for tele-health applications in the realm of affective medicine.
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