SBIR Phase I: Lower Extremity Injury Prediction Neuromuscular Algorithm

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project is provision of a scientifically-based, muscle-specific, mobile first, injury reduction protocol for professional and collegiate athletes who experience lower distal non-contact injuries. This application will overcome barriers to currently available stationary technologies by enabling key insights to relieve athletes before the incident of injury occurs. 60% of injuries for professional and collegiate sports are related to the lower extremities, costing professional organizations $10 M annually in recovery and salary sunk cost and consequently a $1.2 B injury market. We will develop a solution to the prevention care triple aim of 1) Improving the accuracy of diagnosing injuries; 2) Improving treatment recommendations accuracy; 3) Forecasting specific muscle and joint injuries based on the environment. The application is a timely business opportunity starting with a recurring software subscription model that will provide better service, at a lower occurrence of non-contact lower distal injuries. This Small Business Innovation Research (SBIR) Phase 1 Project will translate the core modules of neuromuscular activity and computer vision into a comprehensive, athlete-centered, mobile friendly solution to reduce ankle sprains and ACL tears. We hypothesize that the use of computer vision in combination with the analysis of neuromuscular activity will result in a machine learning human movement index with the ability to model neuromuscular work and approximate time of muscle fatigue. We have demonstrated that the use of the neuromuscular activity data illuminates a direct line of pathology of injury for the main stabilizers within the lower extremity. Testing over 300 athletes and collecting 36 million neuromuscular data samples, we have discovered risk factors that correlate with injuries. By utilizing technologies such as tensor flow, neuromuscular activity muscle activity produces a rich data set identifying muscle and joint inefficiencies via camera on the mobile phone.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.