Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles

Senanayake, S. M. N. Arosha; Triloka, Joko; Malik, Owais Ahmed; Iskandar, Mohammad

doi:10.1109/ijcnn.2014.6889899

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…joint angles/positions) to build a classification model for normal and abnormal gaits. Supervised machine learning techniques such as support vector machine (SVM), knearest neighbor (KNN) and artificial neural network (ANN) have been used previously for identifying abnormal gait [7,8,12,16,17]. The supervised learning techniques generally require ample training data for both classes (normal/abnormal) which may not be readily available for patients having specific type of injuries or surgeries.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

Malik¹

2021

IJCDS

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Section: Literature Reviewmentioning

confidence: 99%

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

Malik¹

2021

IJCDS

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“…The authors used wavelet transform for pre-processing the sEMG signal and an AR model to train the ANN. Senanayake et al [79] used EMG RMS value and soft tissue deformation parameter (STDP) extracted from the video recordings to train a feed-forward-backward propagation neural network (FFBPN) to identify gait patterns. The proposed evaluation scheme improved classification accuracy between healthy and injured subject's gait patterns as Vastus Medialis and Lateralis revealed higher positive correlation between EMG and STDP for healthy individuals [79].…”

Section: Supervised Learningmentioning

confidence: 99%

A Review of EMG Techniques for Detection of Gait Disorders

Singh¹,

Iqbal²,

White³

et al. 2019

Artificial Intelligence - Applications in Medicine and Biology

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Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices.

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Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning

Abraham

Bromberg

Forradellas

2015

Lecture Notes in Computer Science

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Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles

Cited by 6 publications

References 22 publications

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

Deep Autoencoder for Identification of Abnormal Gait Patterns Based on Multimodal Biosignals

A Review of EMG Techniques for Detection of Gait Disorders

Arm Muscular Effort Estimation from Images Using Computer Vision and Machine Learning

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