Abstract
The command of a microprocessor-controlled lower limb prosthesis classically relies on the gait mode recognition. Real time computation of the pose of the prosthesis (i.e., attitude and trajectory) is useful for the correct identification of these modes. In this paper, we present and evaluate an algorithm for the computation of the pose of a lower limb prosthesis, under the constraints of real time applications and limited computing resources. This algorithm uses a nonlinear complementary filter with a variable gain to estimate the attitude of the shank. The trajectory is then computed from the double integration of the accelerometer data corrected from the kinematics of a model of inverted pendulum rolling on a curved arc foot. The results of the proposed algorithm are evaluated against the optoelectronic measurements of walking trials of three people with transfemoral amputation. The root mean square error (RMSE) of the estimated attitude is around 3°, close to the Kalman-based algorithm results reported in similar conditions. The real time correction of the integration of the inertial measurement unit (IMU) acceleration decreases the trajectory error by a factor of 2.5 compared to its direct integration which will result in an improvement of the gait mode recognition.
Highlights
Over the past decade, prosthetic devices controlled by microprocessor have improved the quality of life of people with lower limb amputation [1]
Kalman-based algorithms are often taken as a reference for attitude estimation using inertial measurement unit (IMU) with root mean square error (RMSE) on the orientation of the trunk during gait reported to be as small as 1◦ [17]
In this study an algorithm was developed for real time pose estimation, with consideration for computation power limitation in an embedded system
Summary
Prosthetic devices controlled by microprocessor have improved the quality of life of people with lower limb amputation [1] These devices use different sensors to adapt their behavior to varying terrain. A family of methods based on the complementary filter [4,7] have been proposed to quantify the orientation with an accuracy equivalent to the Kalman-based algorithms and an easier real time implementation. This kind of approach has already been evaluated to quantify the orientation of the lower trunk during gait [8]
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