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

Abstract

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.

A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients

“…Technological progress has led to more sophisticated sensors [ 9 , 36 , 37 , 38 ], devices [ 8 , 28 ] and methodologies [ 18 , 21 , 30 ] to investigate pathological conditions and to provide improvements in rehabilitation outcomes. The PoC presented in this study is a functional proposal for rehabilitation purposes, with the use of HL2 as a body movement sensor for patients with neurological pathology.…”

A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks

“…Additionally, the model's regression and prediction accuracy is rather excellent. The article presents the outcomes of eight of the most prevalent physical training exercise situations used in post-stroke rehabilitation approach [22]. While completing various rehabilitation activities, depth sensors reliably detected critical aspects of the subject's posture.…”

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks

“…In this context, skeletal motion analysis, as an important research direction within computer vision, aims to identify ongoing actions such as jumping, clapping, and making phone calls from continuous human skeletal point data. This technology not only finds widespread applications in intelligent surveillance (Sreenu and Durai, 2019 ), human-computer interaction (Yun et al, 2021 ), and virtual reality (Maskeliūnas et al, 2023 ), but also holds significant importance and challenges in areas such as optimizing sports training and preventing injuries.…”