Confidential (healthcare) · Healthcare
A full-stack AI platform for adaptive rehabilitation. Daily logs adjust the plan automatically, and AI handles intake, plans and video feedback.
Category
AI
Year
2024
Industry
Healthcare
Optimization
Right tool for the task
How it is optimized
Right tool for the task
Rules adapt the plan each day. The model does what needs judgement.
Daily logs feed progression and regression rules, so the plan adapts automatically instead of waiting for a manual review. AI is used where it earns its place: intake, plan generation informed by uploaded injury articles, and feedback on exercise videos.
What keeps it lean
- 01Progression and regression by rule
- 02Plans informed by uploaded source articles
- 03AI feedback on submitted videos
Rules and ordinary software before AI. A model only where it earns its place.
The challenge
The rehabilitation journey was fragmented: manual intake, a movement assessment, a generic exercise plan and infrequent follow-up. Between supervised sessions patients had little guidance, and plans did not respond when a patient improved or struggled. Recovery needed to be one connected journey that adapted every day.
The work
We built the journey end to end: an intake agent, guided movement assessment with instructional videos, automatic exercise plan generation informed by uploaded injury articles, daily logging, and AI feedback on uploaded exercise videos. Progression and regression are rules driven by the daily log; the model is used where judgement is needed, in intake, plan generation and video feedback.
Inside the product
More than one screen.
3 more screens from AI Physiotherapy Assistant, captured at device size.
Movement assessment
Pose analysis on an uploaded clip, with joint angles and form feedback.
Eight-week plan
Phases, this week’s sessions and every adaptation with its reason.
Clinician view
Patients by attention needed and AI plan changes waiting for approval.
On the phone
Home
Movement assessment
Eight-week plan
Clinician view
How it is built
Architecture
A React frontend connects to a Python and FastAPI backend. LangChain coordinates intake, assessment context and plan generation, with OpenAI producing adaptive guidance. Video uploads feed the form-analysis workflow, and uploaded injury articles provide source material for plan generation.
Key features
Challenges & solutions
Challenge
Static exercise plans could not respond when a patient’s daily performance improved or declined.
Solution
Connected daily logs to progression and regression rules so the plan adapts automatically instead of waiting for manual review.
Challenge
Plan generation needed injury-specific context rather than relying on generic model knowledge.
Solution
Made uploaded articles on common injuries available to the generation workflow so recommendations can use relevant source material.
The impact
What became easier, better or more effective.
Patients now have guidance every day, not just at supervised sessions. The plan adapts to how they actually performed, form feedback arrives on the clips they upload, and the clinician reviews joint angles and corrections instead of guessing what happened between appointments.
90%+
accuracy on movement and form checks
Pose analysis on uploaded clips returns joint angles and corrective feedback the clinician can review before it reaches the patient.
8 weeks
of plan, adapting daily
Daily logs drive progression or regression automatically instead of waiting for the next appointment.
1
connected journey from intake to recovery
Intake agent, assessment, plan, daily logging and AI video feedback replace four disconnected steps.
Stack
Frontend
- React
Backend
- Python
- FastAPI
AI
- LangChain
- OpenAI
Optimization
Right tool for the task
Rules and ordinary software before AI. A model only where it earns its place.
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