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BackgroundIn transfemoral (TF) amputees, the forward propulsion of the prosthetic leg in swing has to be mainly carried out by hip muscles. With hip strength being the strongest predictor to ambulation ability, an active powered knee joint could have a positive influence, lowering hip loading and contributing to ambulation mobility. To assess this, gait of four TF amputees was measured for level walking, first while using a passive microprocessor-controlled prosthetic knee (P-MPK), subsequently while using an active powered microprocessor-controlled prosthetic knee (A-MPK). Furthermore, to assess long-term effects of the use of an A-MPK, a 4-weeks follow-up case study was performed.MethodsThe kinetics and kinematics of the gait of four TF amputees were assessed while walking with subsequently the P-MPK and the A-MPK. For one amputee, a follow-up study was performed: he used the A-MPK for 4 weeks, his gait was measured weekly.ResultsThe range of motion of the knee was higher on both the prosthetic and the sound leg in the A-MPK compared to the P-MPK. Maximum hip torque (HT) during early stance increased for the prosthetic leg and decreased for the sound leg with the A-MPK compared to the P-MPK. During late stance, the maximum HT decreased for the prosthetic leg. The difference between prosthetic and sound leg for HT disappeared when using the A-MPK. Also, an increase in stance phase duration was observed. The follow-up study showed an increase in confidence with the A-MPK over time.ConclusionsResults suggested that, partially due to an induced knee flexion during stance, HT can be diminished when walking with the A-MPK compared to the P-MPK. The single case follow-up study showed positive trends indicating that an adaptation time is beneficial for the A-MPK.

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The registration of plantar pressure images is a widely used technique to support human gait analysis. In plantar pressure images, most of the time conventionally derived features are used for further processing. Recently, automatic feature extraction based on PCA and kPCA is being used, to increase the information that can be extracted from this data. In this paper, we describe our work flow and a case study on the application of predicting two pressure features and a non-pressure feature out of the automatically derived PCA features. This includes the normalization of the pressure images, the PCA based feature extraction, and building and testing the regression model based on a linear and kernel SVM.