Gradient-Based Multi-Objective Feature Selection for Gait Mode Recognition of Transfemoral Amputees

Abstract

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and minimum complexity for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14%±1.51% and 98.45%±1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Gradient Descent-Based Optimization Method of a Four-Bar Mechanism Using Fully Cartesian Coordinates

“…To guarantee the dynamic balancing of the mechanism, ShM must be kept constant: The time-derivation of H (Equations (28) and (29)) should be zero.…”

“…The optimization theory deals with selecting the best alternative in the sense of the given objective function [27]. It can be applied to solve a wide variety of problems, for example: [28][29][30].…”

“…Gradient Descent is an iterative algorithm that finds a local minimal in a function [21,27,28]. It starts in a random point and continues until the minimal is reached.…”

A Survey of Human Gait-Based Artificial Intelligence Applications

“…To achieve the highest possible accuracy, usually, more complex sensing technology is required. The goal is to apply feature reduction and selection to avoid redundant features such that only the significant features are extracted from the minimum sensing hardware ( Khademi et al, 2019 ). Information Gain (IG) and not PCA is used to improve interpretability by preserving and ranking the original features ( Onodera et al, 2017 ).…”

Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices

“…Additionally, we presented work to use depth sensing for gait segmentation, in addition to inertial sensors, to improve timing of steps, which ultimately should improve forward prediction as well [15]. Recently, several other research groups have presented preliminary results for the development of other predictive systems for powered prostheses or exoskeletons that use environmental sensors, including [11,16,17,18,19,20,21,22,23,24,25]. These works are important and show the utility of using environmental based sensors for improving high-level control of assistive devices.…”