Evaluation of the PhySens as a Wrist-Worn Wearable in Pitch Detection and Biomechanical Workload Estimation

Abstract

Background:

The volume and frequency of throwing activity are among the most significant risk factors for developing overuse injuries in youth athletes. Despite introducing systematic guidelines for ‘pitch counts,’ throwing injuries continue to rise. Using technology to create enhanced measures of workload exposure in this unique population of athletes may help generate more effective and personalized injury prevention strategies.

Hypothesis:

The wrist-worn sensor system (PhySens) will: 1) accurately detect and differentiate throwing activity from other baseball movements, and 2) accurately predict ball velocity, arm slot angle, and elbow valgus torque.

Study Design:

Descriptive laboratory study.

Level of Evidence:

Level 5.

Methods:

Youth pitchers (n = 10) performed a standardized protocol of pitching, field-throwing, and batting. Pitching velocity and biomechanical data were simultaneously captured by the PhySens and traditional 3-dimensional motion capture. The accuracy of the pitching detection algorithm (throw vs batting) was analyzed by comparing truth data with throwing events cataloged by the device. Ball velocity, elbow valgus torque, and arm slot angle predictions were assessed with Pearson correlation coefficients and Bland-Altman plots.

Results:

A total of 230 events (pitches and bat swings) were analyzed. Pitch detection was excellent, with a sensitivity of 99.4% and specificity 97.9%. Pearson correlations were significant and excellent across all predicted variables, with ball velocity r = 0.96, elbow valgus torque r = 0.95, and arm slot angle r = 0.87. The system demonstrated excellent estimations of ball velocity, elbow valgus torque, and arm slot angle.

Conclusion:

This novel single-sensor wrist worn device was highly accurate in detecting pitching events, predicting ball velocity, and estimating arm slot angle and elbow valgus torque.

Clinical Relevance:

Throwing volume is highly associated with overuse injuries in youth baseball players. Sensor-based measures of workload monitoring can address inherent limitations related to human error and underestimation of true throwing exposure.