Generating an Adaptive and Robust Walking Pattern for a Prosthetic Ankle-Foot by Utilizing a Nonlinear Autoregressive Network With Exogenous Inputs

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 11; pp. 6297 - 6305
• Main Authors: Kouzbary, Hamza Al, Kouzbary, Mouaz Al, Tham, Lai Kuan, Liu, Jingjing, Shasmin, Hanie Nadia, Abu Osman, Noor Azuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Angular position; Angular velocity; Ankle; Artificial neural network (ANN); Computer applications; Control algorithms; Control systems; Feet; Foot; Gait; hierarchical control system; high-level control system; Legged locomotion; Mechanical sensors; Neural networks; nonlinear autoregressive network with an exogenous input (NARX) network; pattern generator; powered ankle-foot; Prostheses; Prosthetics; Recurrent neural networks; Rehabilitation; Rehabilitation robots; Robotics; Root-mean-square errors; Sensors; Switches; Walking; Wearable technology
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3076060

One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1° ± 1.7°. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-value).