Towards an algorithm to optimise gait pattern for patients with above knee prosthesis with the use of inertial sensors

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Misalignment in prosthetic legs can lead to bad posture, back pain and stump problems. A big influence therein is the alignment in the frontal and transverse plane of a prosthetic knee. This work aims to create an auxiliary tool for the prosthetist to align the prosthesis in an optimal way for the patient, with the use of inertial sensors. The goal is to estimate the current alignment in the frontal and transverse plane of the individual’s knee and therewith identify the changes that should be made to achieve an optimal swing of the shank.
A forward kinematic model of the swing phase of a prosthetic knee is combined with an inverse kinematic model to estimate the adjustment setting of the individual’s knee. This is done with the data from two inertial sensors on thigh and shank of the prosthetic leg of the patient. Furthermore, the desired alignment that creates an optimal swing phase is estimated. With the comparison of the estimated and the desired alignment, an adjustment proposition is calculated. In addition, a sensitivity analysis on the sensor-body orientation is conducted. The results for the current alignment setting show a rough accumulation around the expected linear trend. Deviations and outliers are explained with mistakes during the measurement and errors in the data processing. Also, the calculation of the optimal alignment angles and proposed changes show promising results. The results for the sensitivity analysis on the sensor- body orientation show a linear trend. However, the slope is much smaller than the expected 1. This means that disturbances in the sensor-body orientation have a smaller influence on the results of the estimated alignment angles than assumed. The influence on the adjustment angle in the transverse plane is even smaller than on the one in the frontal plane. These results lead to the conclusion that there are additional factors with an impact on the calculations.The basis towards a working algorithm is laid out. Future work on eliminating sources of error in the data processing is suggested. Among other things, a robust approach to define the walking direction has to be established. Further, an additional measurement with a motion capture system is recommended to create a better foundation for further analysis.