GMH-D: Combining Google MediaPipe and RGB-Depth Cameras for Hand Motor Skills Remote Assessment

Impairment in the execution of simple motor tasks involving hands and fingers could hint at a general worsening of health conditions, particularly in the elderly and in people affected by neurological diseases. The deterioration of hand motor function strongly impacts autonomy in daily activities and, consequently, the perceived quality of life. The early detection of alterations in hand motor skills would allow, for example, to promptly activate treatments and mitigate this discomfort. This preliminary study examines an innovative pipeline based on a single RGB-Depth camera and Google MediaPipe Hands, that is suitable for the remote assessment of hand motor skills through simple tasks commonly used in clinical practice. The study includes several phases. First, the quality of hand tracking is evaluated by comparing reconstructed and real hand 3D trajectories. The proposed solution is then tested on a cohort of healthy volunteers to estimate specific kinematic features for each task. Finally, these features are used to train supervised classifiers and distinguish between “normal” and “altered” performance by simulating typical motor behaviour of real impaired subjects. The preliminary results show the ability of the proposed solution to automatically highlight alterations in hand performance, providing an easy-to-use and non-invasive tool suitable for remote monitoring of hand motor skills

A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects

Parkinson's disease (PD) severity is assessed through a set of standardised tasks defined by clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). In particular, Leg Agility is a well-established test among the motor tasks included in UPDRS, which consists in repeated cycles of knee lifting and lowering, while sitting on a chair. Leg Agility objective evaluation through optical devices is often investigated for telemedicine applications. Moreover, remote rehabilitation for PD subjects through virtual exergaming is becoming a popular approach thanks to its versatility, increased user engagement and the possibility of coupling it with remote monitoring tools. This work investigates if lower-limb exergaming may also be exploited for assessment purposes similar to traditional evaluation. In particular, if there exists a statistical difference between the kinematic description of Leg Agility versus the one of a Bouncing Ball exergame, as provided by an optical (RGB-D) acquisition system suitable for remote monitoring. Preliminary results obtained by the comparison of the two types of assessment in a small group of parkinsonian subjects are presented and discussed