Total Rehab
Digital Physiotherapy App
Project overview
Total Rehab is Digital Physiotherapy App. Sports Injury and Post-Surgery Rehabilitation programs are designed with you in mind. Built with high standards for performance, security, and intuitive user experiences, the platform is engineered to support mission-critical workflows and seamless scale.
What was the problem or pain point
Client Dilemma & Technical Bottleneck
Total Rehab sought to transform musculoskeletal physical therapy and orthopedic rehabilitation by creating an intelligent digital clinic that patients could use at home. Patients undergoing physical therapy frequently perform prescribed exercises with improper posture or abandon home regimens entirely due to lack of guidance, resulting in prolonged recovery times, reinjury, and high healthcare costs. Physical therapists lacked objective data on whether patients were completing exercises correctly between clinical visits.
What we suggested to the client
Solid Sphere Architectural Recommendation
Solid Sphere proposed building an AI-powered physical therapy application utilizing on-device computer vision. We recommended developing a mobile app that tracks patient skeletal joints in real time through the smartphone's front-facing camera, providing instant audio-visual posture correction feedback, counting repetitions accurately, and logging range-of-motion metrics to a HIPAA-compliant therapist portal.
What made this project challenging
Guiding physical therapy patients through home rehabilitation exercises with real-time form correction
Using mobile device cameras to track skeletal joint angles without requiring wearable sensors
Ensuring patient compliance and tracking range-of-motion recovery over multi-week treatment plans
Providing physical therapists with objective motion analytics and automated progress reports
What was implemented and how it was implemented
Step-by-Step Technical Execution
We developed the mobile application using React Native with custom native camera bridges for iOS and Android. We integrated lightweight computer vision pose estimation models (MoveNet / MediaPipe via TensorFlow Lite) that run at 30fps directly on mobile device processors without sending video feeds to external servers, guaranteeing total patient privacy. As the patient performs squats, shoulder abductions, or knee extensions in front of their phone, the app calculates joint angles, detects postural compensations (such as excessive lumbar flexion), and offers spoken cues like 'Keep your chest lifted.' On the backend, we constructed a HIPAA-compliant Node.js and PostgreSQL system that aggregates exercise compliance, joint range-of-motion progression, and pain scores. Physical therapists log into a responsive web portal to review patient progress graphs, adjust exercise difficulty, and schedule telehealth check-ins.
How we delivered
Engineered on-device pose estimation pipeline running TensorFlow Lite at 30 FPS on smartphones
Built real-time audio and visual coaching cues alerting patients of improper joint alignment
Developed therapist portal for assigning customized exercise regimens and monitoring progress
Implemented secure video exercise recording and range-of-motion improvement trajectory graphs
What did we use to implement it
Technology Stack & Technical Rationale
React Native provided a unified, accessible mobile patient interface. TensorFlow Lite and Computer Vision Pose Estimation models executed real-time skeletal tracking at 30fps on-device. Node.js delivered the clinical backend API with secure patient session management. PostgreSQL with field-level encryption stored patient exercise metrics, range-of-motion logs, and therapy plans. AWS HIPAA-compliant infrastructure ensured strict healthcare regulatory compliance.
Key outcomes
Over 85% patient adherence to home physical therapy programs (vs 35% industry baseline)
Assisted recovery for over 15,000 orthopedic patients
Objective joint measurement accuracy validated against clinical goniometers
Full HIPAA compliance and secure patient data encryption
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