Abstract
Home based telerehabilitation has expanded after COVID-19, but delivering timely guidance and monitoring exercise performance outside the clinic remains difficult. Traditional physiotherapy often relies on repeated execution of simple routines, yet clinicians have limited visibility into adherence and movement quality during unsupervised sessions. From a systems perspective, many telerehabilitation approaches also face constraints in accessibility, bandwidth, and computational cost that can limit practical deployment. This paper presents a modular telerehabilitation framework and prototype that captures and records rehabilitation exercise sessions for asynchronous clinician review in a 3D visualisation environment. The system integrates skeletal motion capture with plantar pressure sensing, and stores sessions as portable artefacts to support replay, inspection, and downstream analysis. A connector-based architecture enables extension to additional sensors without redesigning the core application, and the design aims to support deployment under constrained home computing and networking conditions. The manuscript contributes an implementation blueprint and reference architecture for multimodal capture and replay. Clinical effectiveness, usability outcomes, and quantitative sensor accuracy benchmarking are outside the scope of this work and are identified as necessary future evaluation.
| Original language | English |
|---|---|
| Article number | 84 |
| Pages (from-to) | 84 |
| Number of pages | 1 |
| Journal | Big Data and Cognitive Computing |
| Volume | 10 |
| Issue number | 3 |
| Early online date | 8 Mar 2026 |
| DOIs | |
| Publication status | Published - 8 Mar 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Telerehabilitaion
- Modularity
- Motion capture
- Insole sensors
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