Development of an AI-Powered Mobile Pose Analysis Application for Remote Rehabilitation

Patients with mobility challenges and new or ongoing rehabilitation needs face barriers such as long wait times and limited access to specialized physiotherapy facilities. This leads to inconsistent exercise execution at home, increasing the risk of injury or ineffective treatment outcomes. The lack of real-time feedback mechanisms hampers patients' ability to perform exercises correctly without direct supervision.

A mid-sized healthcare technology startup focused on developing accessible physiotherapy and fitness solutions via mobile applications.

• Develop a mobile application capable of real-time human pose detection using only the smartphone camera.
• Implement advanced algorithms to accurately assess exercise technique and identify common mistakes to prevent injury.
• Create an intuitive interface that guides users through exercises, offering real-time correction feedback.
• Ensure the system maintains high accuracy and stability despite limitations of mobile device hardware, such as camera quality and processing power.
• Support accessibility for users with varying mobility levels and exercise needs.
• Facilitate iterative development with plans for future improvements in calibration and detection precision.

• Real-time pose tracking using smartphone camera input
• Analysis of joint angles and body positions to evaluate exercise form
• Guidance system to alert users of incorrect movements or postures
• Pre-exercise calibration step to optimize pose detection accuracy
• User-friendly UI for exercise instructions and feedback display
• Data collection for performance monitoring and progress tracking
• Compatibility across common mobile operating systems

• Mobile device camera APIs for live video capture
• cloud-based storage or data analytics platforms for user performance tracking
• Patient management and authentication systems for user account handling

• High detection accuracy with minimal false positives/negatives, targeting >90% correctness in exercise form assessment
• Real-time processing latency under 300 milliseconds to ensure immediate feedback
• App should work reliably across different smartphone models and camera qualities
• Secure handling of user data complying with relevant privacy standards
• Scalable architecture to support expanding user base and feature set

Implementation of this advanced pose detection application is expected to improve exercise accuracy and safety for users performing physiotherapy exercises at home. It aims to reduce injury risks and enhance treatment adherence, resulting in higher patient satisfaction and better health outcomes. The solution will enable wider accessibility to rehabilitation services, potentially decreasing healthcare costs by reducing in-clinic visits and expediting recovery periods. Scalable and adaptable technology will position the company as an innovative leader in remote healthcare solutions, facilitating growth in user engagement and market reach.