LibraryFamily 16 · Rehabilitation & Allied Health
Wearable-guided post-stroke rehabilitation coach
Under reviewPilotEmerging evidence
Undergoing in-depth clinical, technical and governance review.
Uses wearable motion and HRV data plus a daily symptom check-in to recommend the next rehabilitation exercise and flag clinically-relevant deterioration to the physiotherapist.
Plain-language summary
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Clinical context
- Clinical problem
- Monitor a patient's status
- Point of care
- Follow-up
- Nature of AI output
- A recommendation
- Clinical specialty
- Rehabilitation
- Care setting
- Home / remote
- Patient population
- Adults discharged after acute ischaemic stroke into community rehabilitation.
- Intended use
- Uses wearable motion and HRV data plus a daily symptom check-in to recommend the next rehabilitation exercise and flag clinically-relevant deterioration to the physiotherapist.
Technology
- AI technique
- Classical machine learning, Statistical model
- Input data
- Vital signs, Waveforms (ECG, EEG…), Patient-reported data
- Output type
- Recommendation
- Autonomy level
- Informs a human (advisory)
- Model provenance
- Vendor proprietary
- Model version
- rehab-2.1
- Built on a general-purpose model
- No
Deployment
- Status
- Pilot
- Country
- Denmark
- Deployment date
- 20 February 2025
- Sites
- 1
Regulatory & governance
- EU AI Act risk tier
- Limited risk
- High-risk basis
- Not applicable
- Medical device
- No
- EU MDR class
- Not a device
- CE marking
- Not required
- FDA status
- Not applicable
- ISO 14971 risk class
- Low
- GDPR processing basis
- Consent
- GDPR DPIA
- Completed
- Data identifiability
- Pseudonymised
- Explainability method
- Intrinsic
- Human oversight model
- Physiotherapist remains the lead clinician; AI suggestions are framed as options.
NICE evidence standards
- ESF tier
- Tier B — inform / monitor
- Evidence category
- Category 2
Performance summary
- Headline metric
- Time saved
- Value
- 12
- Subgroup performance assessed
- Yes
- Known bias signals
- —
Similar deployments
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Evidence records
Studies and evaluations attached to this use case.
Retrospective validation
Accuracy: 0.84
Population: Post-stroke community cohort, 600 patients
Contributors
- Deploying organisation
- [demo] Rigshospitalet · Hospital / health system · Denmark
- AI vendor
- —
- Product name
- —