Human Walking Gait Classification Utilizing an Artificial Neural Network for the Ergonomics Study of Lower Limb Prosthetics
نویسندگان
چکیده
Prosthetics and orthotics research, studies, technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics usage in daily life. demand increasing due certain factors. One of factors vascular-related disease, which leads amputation. Prosthetic can increase an amputee’s quality Therefore, studies ergonomic design are important. The factor delivers prosthetic products that comfortable for use. way incorporate by studying human walking gait. This paper presents a multiclassification gait based on electromyography (EMG) signals using machine learning method. An EMG sensor was attached bicep femoris longus gastrocnemius lateral head acquire signal. experiment conducted volunteers during normal activity at various speeds movements were segmented as initial contact, labeled gait; loading response terminal stance, mid-gait; pre-swing swing, final signal then characterized artificial neural network (ANN) compared six training accuracy methods, i.e., Levenberg–Marquardt backpropagation algorithm, quasi-Newton method, Bayesian regulation gradient descent backpropagation, with adaptive rate one-step secant backpropagation. study performed well classification three classes overall (training, testing, validation) 96% data will be used explore lower limb future research.
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ژورنال
عنوان ژورنال: Prosthesis
سال: 2023
ISSN: ['2673-1592']
DOI: https://doi.org/10.3390/prosthesis5030046