A novel supervisory control scheme to tackle variations in step length for walking with powered ankle prosthesis. (September 2018)

• Record Type: Journal Article
• Authors: Sahoo, Saikat; Pratihar, Dilip Kumar; Mukhopadhyay, Sudipta
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 212
• Page End: 220
• Publication Date: 2018-09
• Subjects: Step length prediction -- Powered ankle prosthesis -- Adaptive neuro-fuzzy inference system -- Multisensory data fusion -- Supervisory control; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.08.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Step length adaption while using a powered ankle prosthesis. ANFIS with multisensory approach for prediction of step length. Merging of prediction algorithm with inverse pendulum model of biped walking. Simplicity, good accuracy and robustness. Abstract: In daily-life activities, it becomes essential to modulate the walking speed, when an amputee wishes to walk outdoor wearing his/her powered prosthesis. Different walking speeds can be achieved by varying the step length or stride length, and ankle torque requirement during push-off varies significantly with the desired step length. Thus, in order to mimic the natural gait for varying step lengths, it is essential to develop a control algorithm that can predict the amputee's desired step length and modulate the ankle torque of a powered ankle prosthesis, accordingly. Therefore, in this study, a control scheme has been proposed to solve the said purpose. The prediction of step length is achieved with the help of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and multisensory data fusion. Experiments are carried out on walking of four healthy adults and a large amount of data are collected to train the ANFIS. After the training is over, the trained fuzzy inference system is used to evaluate the efficiency of the algorithm for a set of test data. Finally, the predicted step length value is used to calculate the required force during push-off (where the prosthesis has to be put) with the help of biped walking dynamics. Thus, the novelty of this study lies with the proposal of a new strategy for deciding adaptive step length and control algorithm. The key features of this algorithm include its simplicity, good prediction accuracy, ability to tackle uncertainty or imprecision and fast response.