Wearable System for Evaluating Joint Performance and Function

A non-transitory computer readable storage medium comprising stored instructions, which when executed by at least one processor, cause the processor to:

• receive, at a remote server, sensor data describing movement of a joint of a user covered by a plyowrap, wherein the sensor data is collected by an array of tension sensors embedded into the plyowrap across one or more horizontal axes and one or more vertical axes;
• input the received sensor data to a machine learning model to generate a prediction of the performance of the joint, wherein the machine-learned model is trained using a training dataset of historical sensor data collected from a population of users, each entry of the training data comprising historical sensor data collected for a joint and labeled with an identifier of the joint and a known movement of the joint;
• categorize, based on the predicted performance of the joint, a risk level for biomechanical compromise of the joint;
• determine adjustment parameters for the brace based on the categorized risk level for biomechanical compromise of the joint;
• and
• transmit, to a local controller coupled to the plyowrap, instructions to adjust structural properties of the brace based on the determined adjustment parameters, wherein the instructions activate electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap.

A brace feedback system comprising:

• a joint performance evaluator configured to:
• receive, at a remote server, sensor data describing movement of a joint of a user covered by a plyowrap, wherein the sensor data is collected by an array of tension sensors embedded into the plyowrap across one or more horizontal axes and one or more vertical axes;
• input the received sensor data to a machine learning model to generate a prediction of the performance of the joint, wherein the machine-learned model is trained using a training dataset of historical sensor data collected from a population of users, each entry of the training data comprising historical sensor data collected for a joint and labeled with an identifier of the joint and a known movement of the joint;
• categorize, based on the predicted performance of the joint, a risk level for biomechanical compromise of the joint;
• a joint stabilization module configured to:
• determine adjustment parameters for the brace based on the categorized risk level for biomechanical compromise of the joint;
• and
• transmit, to a local controller coupled to the plyowrap, instructions to adjust structural properties of the brace based on the determined adjustment parameters, wherein the instructions activate electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap.