| Title |
AI-assisted Physical Therapy for Post-injury Rehabilitation: Current State of the Art |
| Authors |
(Dilliraj Ekambaram) ; (Vijayakumar Ponnusamy) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.3.234 |
| Keywords |
Virtual; Augmented and mixed reality (VR; AR; and MR); Machine learning (ML); Deep learning (DL); Rehabilitation; Physiotherapy; Movement recognition |
| Abstract |
Telemedicine in physical therapy has increased rapidly since the COVID-19 pandemic erupted. Recuperation can help survivors resume their lives by restoring lost skills, regaining independence, and improving their well-being. With the help of innovative technologies, researchers have created new methods to aid clinicians in patient evaluation and assessment, and more people than ever have access to physiotherapy. The focus of this study is the use of deep learning and machine learning algorithms in conjunction with virtual, augmented, and mixed reality (VR, AR, and MR) technologies for experimental analysis to help patients recover from intracranial hemorrhage, stroke, musculoskeletal and neurological trauma, scoliosis, etc. We present evaluation frameworks systematically categorized into three groups: detecting emotions, identifying movements, and mimicking clinical assessments. We also examine the most popular sensors, body regions, and outcome metrics, and we review plans in evaluating AI strategies (from element design to grouping). Finally, some challenges and future directions for reviewing the field are presented. |