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Articles 1 - 3 of 3
Full-Text Articles in Engineering
Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon
Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon
Electronic Thesis and Dissertation Repository
Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.
One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …
A Comparison Of Ground Reaction Forces And Muscle Activity Of The Tsunami Bar® Against A Rigid Barbell During Back Squat Phases, John Carver Middleton
A Comparison Of Ground Reaction Forces And Muscle Activity Of The Tsunami Bar® Against A Rigid Barbell During Back Squat Phases, John Carver Middleton
Theses and Dissertations
An Institutional Review Board (IRB)-approved study was conducted to investigate the effects of the Tsunami Bar® (TB), a flexible barbell, on ground reaction force (GRF) production and muscle activity in the quadricep, hamstring, and gluteal muscle groups during phases of the squat exercise and compare the effects to the effects to using a rigid barbell (RB). A two-by-two repeated measures Analysis of Variance (ANOVA) test was used to compare the results. Descriptive statistics showed significantly higher GRFs for the TB during the unweighting phase, significant differences in GRFs between speeds for each phase, significantly higher forces on average with the …
Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert
Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …