Return to sports using interpretable movement behavior

The complexity of the human locomotive system creates challenges in assessing the quality of an individual's movements. While wearable sensors can provide a wealth of information through the data they collect, interpreting that data presents another challenge. The purpose of this study was to investigate whether Principal Component Analysis (PCA) performed on sensor data collected during locomotor task could identify movement strategies used by individuals returning to sport (return-to-sport; RTS).