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Landing heel first has been associated with elevated external knee abduction moments (KAM), thereby potentially increasing the risk of sustaining a non-contact ACL injury. Apart from the foot strike angle, knee valgus angle (VAL) and vertical center of mass velocity at initial ground contact (IC) have been associated with increased KAM in females across different sidestep cuts. While real-time biofeedback training has been proven effective for gait retraining [4], the highly dynamic, non-cyclical nature of cutting maneuvers makes real-time feedback unsuitable and alternative approaches necessary. This study aimed at assessing the efficacy of immediate software-aided feedback on cutting technique in reducing KAM during handball-specific cutting maneuvers.
In this paper, we propose an approach for gait phase detection for flat and inclined surfaces that can be used for an ankle-foot orthosis and the humanoid robot Sweaty. To cover different use cases, we use a rule-based algorithm. This offers the required flexibility and real-time capability. The inputs of the algorithm are inertial measurement unit and ankle joint angle signals. We show that the gait phases with the orthosis worn by a human participant and with Sweaty are reliably recognized by the algorithm under the condition of adapted transition conditions. E.g., the specificity for human gait on flat surfaces is 92 %. For the robot Sweaty, 95 % results in fully recognized gait cycles. Furthermore, the algorithm also allows the determination of the inclination angle of the ramp. The sensors of the orthosis provide 6.9 and that of the robot Sweaty 7.7 when walking onto the reference ramp with slope angle 7.9.