Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.

Volleyball players often land on a single leg following a spike shot due to a shift in the center of gravity and loss of balance. Landing on a single leg following a spike may increase the probability of non-contact anterior cruciate ligament (ACL) injuries. The purpose of this study was to compare and analyze the kinematics and kinetics differences during the landing phase of volleyball players using a single leg (SL) and double-leg landing (DL) following a spike shot. The data for vertical ground reaction forces (VGRF) and sagittal plane were collected. SPM analysis revealed that SL depicted a smaller knee flexion angle (about 13.8°) and hip flexion angle (about 10.8°) during the whole landing phase, a greater knee and hip power during the 16.83–20.45% (p = 0.006) and 13.01–16.26% (p = 0.008) landing phase, a greater ankle plantarflexion angle and moment during the 0–41.07% (p < 0.001) and 2.76–79.45% (p < 0.001) landing phase, a greater VGRF during the 5.87–8.25% (p = 0.029), 19.75–24.14% (p = 0.003) landing phase when compared to DL. Most of these differences fall within the time range of ACL injury (30–50 milliseconds after landing). To reduce non-contact ACL injuries, a landing strategy of consciously increasing the hip and knee flexion, and plantarflexion of the ankle should be considered by volleyball players.

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