PhysioTrack: An Intelligent AI and IoT System for Smart Physical Rehabilitation
Bibliographic record
Abstract
Physical rehabilitation is crucial for restoring mobility and quality of life after knee injuries, yet patient adherence remains a significant challenge due to monotonous routines and a lack of real-time feedback. This paper introduces PhysioTrack, an intelligent system that integrates Artificial Intelligence (AI), Internet of Things (IoT), and Virtual Reality (VR) to transform traditional physiotherapy into an interactive, adaptive, and data-driven process. The system employs wearable IMU and pressure sensors to capture motion data, which is processed by a lightweight Multi-Layer Perceptron (MLP) model converted to ONNX format for real-time inference on edge devices. Through a gamified VR interface developed in Unity, patients receive immediate audiovisual feedback and guidance from a virtual coach, enhancing engagement and correct exercise execution. Experimental results demonstrate that PhysioTrack achieves 96.2% accuracy in classifying squat exercises and an 80% improvement in user satisfaction compared to conventional methods. The proposed solution highlights the potential of AI-driven, patient-centric systems to enhance the effectiveness, accessibility, and motivation of home-based rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".