Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator.

Body-worn devices have significant potential to improve the health and well-being of many individuals. In this work, wearable inertial sensors are used in order to track and discriminate shoulder motion gestures, without using visual markers or other approaches that constrain the system to a laboratory environment. The device, consisting of a set of orthogonal accelerometers, gyroscopes, and magnetic field sensors, is attached to the person's upper arm to help prevent shoulder over-use injuries in strenuous work and in athletics. The sensor suite is used to track the orientation of the arm as a function of time. We present a detection and classification approach that can be used to evaluate the number of times certain motion gestures occur.