Wyniki wyszukiwania - BazTech

1

Dual-peak cyclic loading for evaluation of lower-limb prostheses

Zhang Zhaokun, Xu Wangrui, Gan Hanyu, Song Chunlei K., Shih Albert J.

Biocybernetics and Biomedical Engineering

|

2025

|

Vol. 45, iss. 4

642--654

EN

Lower-limb prosthesis (LLP) is critical for the mobility and quality of life of amputees. Ensuring the reliability and durability of LLPs is essential to users’ safety, comfort, and mobility. While the current standard (ISO 10328) for the mechanical test of LLPs has served as a foundation, the sinusoidal cyclic loading method used for fatigue testing does not replicate the actual loading conditions on LLPs during a human walking gait. In normal walking, LLPs are subjected to a dual-peak loading condition with two distinct force peaks at the heel-strike and toe-off phases. Such a cyclic and dual-peak dynamic loading pattern on LLP is essential to test the durability of the LLP effectively and reliably. In this study, two simple and effective dual-peak cyclic loading test apparatuses are designed and built to evaluate the durability and functionality of LLPs. Two loading plates are used to contact the heel and toe of the prosthetic foot to simulate the dual-peak heel-strike and toe-off loadings between the foot and the ground. This dual-peak loading on the LLP is controlled and replicated by adjusting the positions of two loading plates and the actuator stroke to change the contact forces. Experimental results show that the proposed testing apparatuses and procedures can emulate the dual-peak axial loading of the LLP during normal walking gait, providing a more accurate testing method of the dynamic loading condition on LLPs than the current ISO standard.

2

Intent based recognition of walking and ramp activities for amputee using sEMG based lower limb prostheses

Hussain Tahir, Iqbal Nadeem, Maqbool Hafiz Farhan, Khan Mukhtaj, Awad Mohammed Ibrahim, Dehghani-Sanij Abbas A.

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 3

1110--1123

EN

The use of surface electromyogram (sEMG) has grown in the field of gait analysis, prostheses and exoskeleton. Surface electromyogram can directly reflect the human intention for locomotion modes and can be used as a source of control for lower limb prosthesis. The variations factors such as non-Gaussian nature of sEMG signal and mobility of amputees have been observed to degrade the activity recognition performance. This study investigates the properties of the sEMG signal with the purpose of determining the discriminant features to classify the feature space into various activities especially in the context of amputees. To address the variations in activity recognition performance, this study proposed the magnitude of bispectrum as a novel feature extraction method that is invariant to the variations factors and an unsupervised feature reduction method was used to extract the discriminant features. Furthermore, sEMG signals from eleven wearable sensors located on the lower limb muscles were recorded from six subjects including four able-bodies, one unilateral transti-bial, and one unilateral transfemoral amputee during walking and ramp activities. Distinct muscles were selected using the L1-norm method. Effective classifier namely support vector machine and linear discriminant analysis were used to classify the multi-class sEMG signal patterns. The experimental results consistently showed an average accuracy of 99.7%. Further evaluation on three different types of prostheses revealed that the proposed method is more robust compared to the existing methods. The promising results of this study can be applied potentially in the control of lower limb wearable devices such as prostheses/ exoskeletons.

3

Single channel EMG-based continuous terrain identification with simple classifier for lower limb prosthesis

Gupta Rohit, Agarwal Ravinder

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 3

775--788

EN

The focus of the present research endeavour is to propose a single channel Electromyogram (EMG) signal driven continuous terrain identification method utilizing a simple classifier. An iterative feature selection algorithm has also been proposed to provide effective information to the classifiers. The proposed method has been validated on EMG signal of fifteen subjects and ten subjects for three and five daily life terrains respectively. Feature selection algorithm has significantly improved the identification accuracy (ANOVA, p-value < 0.05) as compared to principal component analysis (PCA) technique. The average identification accuracies obtained by Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Neural Network (NN) classifiers are 96.83 ± 0.28%, 97.45 ± 0.32% and 97.61 ± 0.22% respectively. Subject wise performance (five subjects) of individually trained classifiers shows no significant degradation and difference in performance among the subjects even for the untrained data (ANOVA, p-value > 0.05). The study has been extended to dual muscle approach for terrain identification. However, the proposed algorithm has shown similar performance even with the single muscle approach (ANOVA, p-value > 0.05). The outcome of the proposed continuous terrain identifi-cation method shows a pronounced potential in efficient lower limb prosthesis control.