Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach based on Azure Kinect

Abstract

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase of patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. Preliminary results show the system’s ability to quantify specific features of motor performance, easily monitor changes and disease progression over time, and the possibility of using exergames to support motor condition assessment and training. The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.

A Model-Based Markerless Protocol for Clinical Gait Analysis Based on a Single RGB-Depth Camera: Concurrent Validation on Patients With Cerebral Palsy

“…The largest errors affecting the ankle kinematics are mainly due to the auto-exposure of the camera which can cause blurred images of the foot and distal part of the shank during fastest movement such as the swing phase. In the last years, several single-camera MS methods were proposed for gait analysis, however, in many cases, a direct comparative evaluation with the proposed method was not possible because: (i) the kinematic outputs were not validated against a clinically-accepted gold standard ([56]- [59]) (ii) the method performance was only validated in terms of accuracy in tracking joint centers ( [46], [60]), (iii) the objective was to classify motor activities or to detect gait abnormalities ( [54], [61]- [65]). To the best of authors knowledge, the only MS study involving CP children was presented by Ma et al [46] that evaluated the concurrent validity of the built-in body tracking SDK (Kinect v2) against an MB gait protocol on 10 CP children (GMFS I-II) based on a frontal view.…”