Gait Phase Discrimination during Kinematically Constrained Walking on Slackline

Singh, Rajat Emanuel; Iqbal, Kamran; Ullah, Safi; Alazzawi, Ahmed; White, Gannon

doi:10.1109/icca.2019.8899952

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Signal processing techniques for feature extraction and selection, and standardization of the time series play a crucial role in enhancing classification accuracy. We also see consistent improvement in the existing models with increased classification accuracy [84]. ANN classifier has some deficiencies, such as high training process time and overfitting.…”

Section: Supervised Learningsupporting

confidence: 68%

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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Section: Supervised Learningsupporting

confidence: 68%

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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“…The use of kinetic, kinematics and neural signals (EMG) is not uncommon for movement classification [13], [14], [15]. Based on the application of our study, the duration and strength of IBMs, kinetics, and kinematics are better choices for signal classification.…”

Section: Discussionmentioning

confidence: 97%

Involuntary breathing movement pattern recognition and classification via force based sensors

Singh

Fleury

Gupta

et al. 2022

Preprint

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The study presents a novel scheme that recognizes and classifies different subphases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in a supine position on force plates. We segmented their data into the no-movement (NM) phase aka easy phase and IBM phase (comprising several events or sub-phases of IBM). The acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. The K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five-six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggle and were labeled as classes for classification using support vector machine (SVM), naive bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had a low computational cost and high robustness of 96–99% accuracy.

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“…Several studies have concluded that three to five muscle synergies are sufficient to account for the basic patterns of muscle activation in the upper and lower limbs ( Ivanenko et al, 2004 ), ( Singh et al, 2019 ), ( Abd et al, 2021a ). In our study, the control participants had four muscle synergies on average for each of the BMCA tasks.…”

Section: Discussionmentioning

confidence: 99%

Epidural stimulation restores muscle synergies by modulating neural drives in participants with motor/sensory complete spinal cord injuries

Singh

Ahmadi

Parr

et al. 2022

Preprint

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Multiple studies have corroborated restored volitional motor control after motor-complete spinal cord injury (SCI) through the use of spinal cord stimulation (SCS/eSCS) but rigorous quantitative descriptions have been lacking. Using a structured surface electromyogram based (sEMG) task with and without SCS during the Epidural Stimulation After Neurological Damage (ESTAND) study in participants with chronic, motor and sensory complete SCI, we investigated muscle activity complexity and muscle synergies to better characterize neuro-muscular control.In addition, competition exists between the task and neural origin hypotheses underlying muscle synergies, and this analysis in humans with motor and sensory complete chronic injury provided an opportunity to test these hypotheses. Muscle activity complexity was computed with Higuchi Fractal Dimensional analysis (HFD), and muscle synergies were estimated using non-negative matrix factorization (NNMF) in six participants with AIS A chronic SCI. We found that the complexity of muscle activity is immediately reduced with SCS in the SCI participants. We also found that over the follow-up sessions, the muscle synergy structure of the SCI participants became more defined, and the number of synergies decreased over time, indicating improved coordination between the muscle groups. Lastly, we found that the muscle synergies were restored with SCS, supporting the neural hypothesis of muscle synergies. We conclude that SCS restores muscle movements and muscle synergies that are distinct from healthy, able-bodied controls.

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Gait Phase Discrimination during Kinematically Constrained Walking on Slackline

Cited by 7 publications

References 14 publications

A Review of EMG Techniques for Detection of Gait Disorders

A Review of EMG Techniques for Detection of Gait Disorders

Involuntary breathing movement pattern recognition and classification via force based sensors

Epidural stimulation restores muscle synergies by modulating neural drives in participants with motor/sensory complete spinal cord injuries

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