Objective: We set up an experimental instrumentation to quantify selected items of UPDRS by a Kinect technology and body sensory-network (BSN), destinated to an easy home-performance.

Background: Remote medical communications in the form of telemedicine is one of the challenges to Parkinson’s disease (PD) problems. Among the tougher hurdles to overcome,there is an accurate, low-cost and manageable quantification of motor symptoms.

Methods: For automatic assignment of UPDRS scores, we studied 20 controls subjects and 64 PD patients both by a BSN-based approach (for leg agility, sit-to stand and gait tasks), composed of a few body-horn wireless inertial nodes and an human-computer interface( Microsoft Kinect®) ((for finger-tapping task) based on a RGB-Depth camera, a monitor and two light-weight gloves with coloured markers. Movements are automatically translated in kinematic parameters and then classified by dedicated algorytms correlating with corresponding UPDRS clinical scores. We calculated the average of the predicted UPDRS classes weigthed by the probabilities that an evaluation belongs to a specific UPDRS classes, by a continuous measure that we call the neuromotor impairment W [figure1]

Results: We found 19 and 34 kinematic parameters respectively both for finger and lower limbs movements correlating which corresponding UPDRS scores tasks [figure2].

Conclusions: These results show that the proposed technology is an accurate, feasible and low-cost approach useful for at distance evaluation of PD patients.

Oral Presentation by Parkinson’s disease World Congress 2016 in Milan.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/a-step-forward-to-the-future-updrs-kinematic-measures-for-telemedicine/