Wrist-Worn Motion Feedback for Orthopedic Therapy Monitoring
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Solution Overview
Problem
Current techniques for orthopedic patient care, particularly for upper extremities, fail to adequately monitor or assess range of motion and pain before or after surgical intervention, leading to potential long-term issues such as 'Frozen shoulder', and do not provide effective feedback during physical or occupational therapy.
Innovation Solution
A system comprising wrist-worn devices and mobile applications that capture and analyze motion data, provide feedback, and suggest interventions, using machine learning and reinforcement learning to track range of motion, pain, and compliance with therapy protocols, without requiring depth cameras or gait labs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques involving immobility, physical therapy, or occupational therapy are used, then surgical intervention can be performed, but range of motion and pain cannot be adequately monitored or assessed
Solution Approach 1:
The patent implements continuous feedback loops where sensor data from wrist-worn devices is processed by machine learning models to generate real-time feedback about range of motion and pain levels. This feedback is provided to both patients and healthcare providers, enabling dynamic adjustment of therapy protocols based on actual physiological responses rather than static assessment schedules.
Solution Approach 2:
The patent replaces traditional mechanical assessment methods (manual goniometry, visual observation) with electronic sensor-based measurement systems. Wrist-worn devices with accelerometers, gyroscopes, and other sensors automatically capture motion data, while machine learning algorithms process this data to derive range of motion and pain metrics, eliminating the need for direct physical measurement by therapists.
2Reliability
If immobilization is applied to prevent post-surgical complications, then joint stability is improved, but long term issues such as Frozen shoulder develop
Solution Approach 1:
The patent transitions from static immobilization protocols to dynamic, adaptive therapy regimens. Machine learning models continuously analyze sensor data to determine optimal balance between immobilization and movement, adjusting recommendations in real-time based on healing progression. This allows the system to provide stability when needed while preventing excessive immobilization that would lead to frozen shoulder.
Solution Approach 2:
The patent implements preventive monitoring that identifies early signs of frozen shoulder development before they become severe. By continuously tracking range of motion and pain levels, the system can alert patients and providers to take preliminary corrective actions (adjusting therapy intensity, modifying exercises) before capsule thickening and stiffness become irreversible.
3Productivity
If physical therapy is used to recover strength and functioning, then patient recovery is improved, but adequate monitoring and assessment of pain and range of motion are not provided
Solution Approach 1:
The patent enables patients to self-monitor and self-report therapy compliance through the mobile application. The system automatically tracks whether prescribed exercises are performed, measures actual range of motion achieved during exercises, and logs pain levels. This self-service approach eliminates the need for therapists to manually observe and record every therapy session, while still providing comprehensive data for assessment.
Solution Approach 2:
The patent introduces a digital intermediary layer between patient and therapist. The mobile application and machine learning models act as intermediaries that continuously collect, process, and analyze therapy data, then present synthesized insights to healthcare providers. This intermediary system maintains detailed records of therapy compliance, pain levels, and range of motion progression without requiring direct therapist involvement in every measurement.
Data Source
AI summary
Systems and methods may be used for presenting motion feedback for an orthopedic patient. In an example, images may be captured of a patient in motion attempting to perform a task, for example after completion of an orthopedic surgery on the patient. The images may be analyzed to generate a movement metric of the patient corresponding to the task. The movement metric may be compared to a baseline metric (e.g., an average metric or a previous patient metric) for the task. An indication of the comparison may be presented, for example including a qualitative result of the comparison.