This predictor introduces machine learning into the domain of physiotherapy and rehabilitation.
The rehabilitation market currently focuses on remote or digital exercise programmes and adherence tracking. Rehab Twin offers predictive analytics and patient specific precision.
Using a wearable continuous vital-sign monitor, this predictor couples real-time data streaming with personalised adaptive learning. Each individual recovering from injury benefits from a digital twin comprised of a continuously learning computational model that predicts short-term physiological change and guides intervention.
This predictor uses an individually personalised adaptive learning approach, dynamically calibrating baselines for each individual and learning how each person reacts to stress, load, and recovery. This establishes a high-fidelity connection between physiology, environment, and action.
Unique predictors address priorities in rehabilitation medicine and physiotherapy for reliable predictive analytics to track the rehabilitative recovery trajectory at an individual level. This predictor uses four signal indicators and predictors to support clinical interventions.
This predictor enables rehab doctors and physiotherapists to predict recovery trajectories, optimise exercise load, and prevent reinjury by using (HR-power ratio) continuous physiological data and digital-twin simulations. It can be applied broadly in post-operative orthopaedic rehabilitation (e.g., total knee or hip replacement), neurological recovery (post-stroke motor rehabilitation) and chronic musculoskeletal disorders (e.g., low-back pain, tendonopathies).
The clinical dashboard displays unique indicators to track the rehabilitation trajectory and take corrective action in advance of emergent exacerbations.
The four rehabilitation metrics are defined uniquely through machine learning.While similar ideas appear in physiotherapy assessment and HRV analysis, these indices represent composite measures combining digital twin simulation and continuous physiological monitoring. They provide predictive rather than descriptive analytics but whichare not yet standardised across the rehabilitation industry.
- Recovery Efficiency Index (REI): ratio of observed to expected recovery gradient
- Re-injury Probability (RP): likelihood of strain or compensatory pattern emergence within 48 hours.
- Exercise Adherence Index (EAI): compares motion-derived exercise compliance to prescribed regimens.
- Autonomic Stress Index (ASI): derived from HRV and galvanic proxies; detects over-exertion or inadequate rest.
The dashboard provides green-amber-red recovery states with trend-lines. Adaptive algorithms suggest exercise modifications (intensity, duration, rest intervals). And alerts are triggered when predicted recovery delay varies more than 15 % from baseline trajectory.
From a practical approach, the goal is to target a 20 to 30% reduction in average rehabilitation duration with reduced therapist time per patient through objective remote monitoring and lower readmission and re-injury rates.
For more information: Dr Michael Tremblay, mike_tremblay@skythunder.net ww.skythunder.net