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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8173
• Main Authors: Amprimo, Gianluca, Masi, Giulia, Priano, Lorenzo, Azzaro, Corrado, Galli, Federica, Pettiti, Giuseppe, Mauro, Alessandro, Ferraris, Claudia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.10.2022; MDPI
• Subjects: Accuracy; Algorithms; Antiparkinsonian agents; azure kinect; Balance; Design; Development and progression; Diseases; exergames; Exergaming; Gait; Humans; Integrated approach; Italy; Movement; movement analysis; neurorehabilitation; Parkinson Disease - diagnosis; Parkinson's disease; Physical therapy; Posture; Quality of Life; Rehabilitation; Sensors; Therapeutics, Physiological; UPDRS; Usability; User interface; Virtual Reality; Italy; azure kinect; telemedicine; exergames; body tracking; UPDRS; movement analysis; Parkinson’s disease; neurorehabilitation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218173

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 in 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. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm 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.