BiomacVR: A Virtual Reality-Based System for Precise Human Posture and Motion Analysis in Rehabilitation Exercises Using Depth Sensors

• Published in: Electronics (Basel) Vol. 12; no. 2; p. 339
• Main Authors: Maskeliūnas, Rytis, Damaševičius, Robertas, Blažauskas, Tomas, Canbulut, Cenker, Adomavičienė, Aušra, Griškevičius, Julius
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Algorithms; Cameras; Deep learning; Effectiveness; Expenditures; Human motion; Information technology; Machine learning; Medical research; Mobility; Motion perception; Neural networks; Older people; Parameter identification; Patients; Personal computers; Physical therapy; Physical training; Posture; Rehabilitation; Remote monitoring; Remote sensors; Response time; Sensors; Smartphones; Stroke; Telemedicine; Virtual reality
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12020339

Remote patient monitoring is one of the most reliable choices for the availability of health care services for the elderly and/or chronically ill. Rehabilitation requires the exact and medically correct completion of physiotherapy activities. This paper presents BiomacVR, a virtual reality (VR)-based rehabilitation system that combines a VR physical training monitoring environment with upper limb rehabilitation technology for accurate interaction and increasing patients’ engagement in rehabilitation training. The system utilises a deep learning motion identification model called Convolutional Pose Machine (CPM) that uses a stacked hourglass network. The model is trained to precisely locate critical places in the human body using image sequences collected by depth sensors to identify correct and wrong human motions and to assess the effectiveness of physical training based on the scenarios presented. This paper presents the findings of the eight most-frequently used physical training exercise situations from post-stroke rehabilitation methodology. Depth sensors were able to accurately identify key parameters of the posture of a person performing different rehabilitation exercises. The average response time was 23 ms, which allows the system to be used in real-time applications. Furthermore, the skeleton features obtained by the system are useful for discriminating between healthy (normal) subjects and subjects suffering from lower back pain. Our results confirm that the proposed system with motion recognition methodology can be used to evaluate the quality of the physiotherapy exercises of the patient and monitor the progress of rehabilitation and assess its effectiveness.