Myoelectric Control for Active Prostheses via Deep Neural Networks and Domain Adaptation

Recent advances in Biological Signal Processing (BSP) and Machine Learning (ML), in particular, Deep Neural Networks (DNNs), have paved the way for development of advanced Human-Machine Interface (HMI) systems for decoding human intent and controlling artificial limbs. Myoelectric control, as a subcategory of HMI sys- tems, deals with detecting, extracting, processing, and ultimately learning from Electromyogram (EMG) signals to command external devices, such as hand prostheses. In this context, hand gesture recognition/classification via Surface Electromyography (sEMG) signals has attracted a great deal of interest from many researchers. De- spite extensive progress in the field of myoelectric prosthesis, however, there are still limitations that should be addressed to achieve a more intuitive upper limb pros- thesis. Through this Ph.D. thesis, first, we perform a literature review on recent research works on pattern classification approaches for myoelectric control prosthesis to identify challenges and potential opportunities for improvement. Then, we aim to enhance the accuracy of myoelectric systems, which can be used for realizing an accu- rate and efficient HMI for myocontrol of neurorobotic systems. Beside improving the accuracy, decreasing the number of parameters in DNNs plays an important role in a Hand Gesture Recognition (HGR) system. More specifically, a key factor to achieve a more intuitive upper limb prosthesis is the feasibility of embedding DNN-based models into prostheses controllers. On the other hand, transformers are considered to be powerful DNN models that have revolutionized the Natural Language Processing (NLP) field and showed great potentials to dramatically improve different computer vision tasks. Therefore, we propose a Transformer-based neural network architecture to classify and recognize upper-limb hand gestures. Finally, another goal of this thesis is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. We propose to solve this problem, by designing a framework which utilizes a combination of temporal convolutions and attention mechanisms

Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography

Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for immersion in Mixed Reality (MR) environments. To achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. In this work, we propose a light-weighted hybrid architecture (HDCAM) based on Convolutional Neural Network (CNN) and attention mechanism to effectively extract local and global representations of the input. The proposed HDCAM model with 58,441 parameters reached a new state-of-the-art (SOTA) performance with 82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying 17 hand gestures. The number of parameters to train the proposed HDCAM architecture is 18.87 times less than its previous SOTA counterpart

DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning

Multi-task networks rely on effective parameter sharing to achieve robust generalization across tasks. In this paper, we present a novel parameter sharing method for multi-task learning that conditions parameter sharing on both the task and the intermediate feature representations at inference time. In contrast to traditional parameter sharing approaches, which fix or learn a deterministic sharing pattern during training and apply the same pattern to all examples during inference, we propose to dynamically decide which parts of the network to activate based on both the task and the input instance. Our approach learns a hierarchical gating policy consisting of a task-specific policy for coarse layer selection and gating units for individual input instances, which work together to determine the execution path at inference time. Experiments on the NYU v2, Cityscapes and MIMIC-III datasets demonstrate the potential of the proposed approach and its applicability across problem domains

Effectiveness of Short-Term Cognitive-Behavioral Group Therapy on Binge Eating Disorder in Females

Purpose: Due to an increasing prevalence of over eating disorders, this paper aims to investigate the effectiveness of short-term group cognitive-behavioral therapy on reducing binge eating behavior and depression symptoms among females suffered from binge eating disorder (BED) in Qazvin, Iran. Methodology: This is aquasi-experimental study (pre-post testing plan with control group). Using a convenience sampling technique, binge eating scale (BES) and clinical interviews, 30 persons were selected among all clients who had referred to weight loss centers in Qazvin, these persons were randomly placed into two control and experimental groups. The experimental group participated in a seven-session plan on short-term cognitive-behavioral group therapy. Results: The results showed that the short term group cognitive-behavioral therapy results in a significant reduction in binge eating signs and depressive symptoms within the experimental group, compared to the control. Conclusion: Regarding the results, it is known that short-term group cognitive-behavioral therapy is effective in reducing overeating symptoms. Hence, it can be used as an economical and effective treatment method for individuals suffering from BED

Effectiveness of Short-Term Cognitive-Behavioral Group Therapy on Binge Eating Disorder in Females

Purpose: Due to an increasing prevalence of over eating disorders, this paper aims to investigate the effectiveness of short-term group cognitive-behavioral therapy on reducing binge eating behavior and depression symptoms among females suffered from binge eating disorder (BED) in Qazvin, Iran. Methodology: This is aquasi-experimental study (pre-post testing plan with control group). Using a convenience sampling technique, binge eating scale (BES) and clinical interviews, 30 persons were selected among all clients who had referred to weight loss centers in Qazvin, these persons were randomly placed into two control and experimental groups. The experimental group participated in a seven-session plan on short-term cognitive-behavioral group therapy. Results: The results showed that the short term group cognitive-behavioral therapy results in a significant reduction in binge eating signs and depressive symptoms within the experimental group, compared to the control. Conclusion: Regarding the results, it is known that short-term group cognitive-behavioral therapy is effective in reducing overeating symptoms. Hence, it can be used as an economical and effective treatment method for individuals suffering from BED

The effect of cognitive-behavioral group therapy on disturbed body image and body dysmorphic disorder among students

Nowadays, body dissatisfaction and disturbed body image is very common among communities, especially women and adolescents. The aim of this research was to determine the effectiveness of cognitive-behavioral group therapy on disturbed body image and body dysmorphic disorder among high school girl students.The design of this study was quasi-experimental research with pretest -post test with control group and follow- up period. A sample include 27 individual who were selected with available sampling method (volunteer) of one of the high schools. Then, the subjects were divided in two groups experimental Group (n= 14), and control Group (n= 13) and assigned in two experimental and control group and were tested by Fisher Body Image questionnaire and Modified Yale- Brown Obsessive-compulsive Scale for body dysmorphic disorder. Then, experimental group received 8 session of cognitive- behavioral therapy techniques and control group not received any treatment. In the end, information from both groups recollected and Follow-up tests were performed two month after the intervention. Data analyzed with Spss-18 software and Multivariate Analysis of Covariance.The findings showed that disturbed body image and body dysmorphic disorder was a significant difference between experimental and control group, in post-test and follow up.This Results study showed that cognitive-Behavioral group therapy is effective in decreasing disturbed body image and body dysmorphic disorder for high school girls. Therefore, it is suggested mental health professionals must be considered to implement and sustain these programs

HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information

Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR) at a macroscopic level (i.e., directly from sEMG signals). At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information. However, due to complexities of sEMG decomposition and added computational overhead, HGR at microscopic level is less explored than its aforementioned DNN-based counterparts. In this regard, we propose the HYDRA-HGR framework, which is a hybrid model that simultaneously extracts a set of temporal and spatial features through its two independent Vision Transformer (ViT)-based parallel architectures (the so called Macro and Micro paths). The Macro Path is trained directly on the pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p values of the extracted Motor Unit Action Potentials (MUAPs) of each source. Extracted features at macroscopic and microscopic levels are then coupled via a Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR framework through a recently released HD-sEMG dataset, and show that it significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR framework achieves average accuracy of 94.86% for the 250 ms window size, which is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively

Comparing the expression levels of mRNA for MMP-7 in gastric mucosa of patients with H. pylori infection and uninfected patients

Background and purpose: The expression of growth factors, proteolytic enzymes, fibrogenic factors, and cytokines are altered in Helicobacter pylori (H. pylori) infected gastric mucosa. Matrix metalloproteinases (MMP) are a family of zinc-dependent homologous enzymes digesting most of the components of the extracellular matrix and basement membrane and are involved in remodeling and functioning of the biological processes. The purpose of this study was to compare gene expression of matrix metalloproteinase-7 (MMP-7) in patients with H. pylori-infected and uninfected individuals with gastrointestinal diseases. Materials and methods: This study was conducted in 50 H. pylori-negative patients and 50 H. pylori-positive patients being admitted to Shahrekord Hajar Hospital due to gastrointestinal diseases in 2014. The participants’ demographic information was collected and sampling was done. First DNA was extracted, and then PCR was performed to check for the presence of 16sRNA and UreC. The RNA from each sample was also extracted and cDNA was prepared. Afterwards, the expression of MMP-7 was measured by real time-PCR using specific primers and probes. Results: MMP-7 mRNA expression was significantly higher in biopsies of H. pylori-infected patients compared to that in H. pylori-uninfected patients (P<0.0001). Conclusion: Increased expression of MMP-7 can be effective in inflammatory response and development of the disease. It could be used as a key marker for early diagnosis of gastrointestinal diseases and gastric cancer. © 2016, Mazandaran University of Medical Sciences. Engineering. All rights reserved

Effectiveness of Short-Term Cognitive-Behavioral Group Therapy on Binge Eating Disorder in Females

Purpose: Due to an increasing prevalence of over eating disorders, this paper aims to investigate the effectiveness of short-term group cognitive-behavioral therapy on reducing binge eating behavior and depression symptoms among females suffered from binge eating disorder (BED) in Qazvin, Iran. Methodology: This is aquasi-experimental study (pre-post testing plan with control group). Using a convenience sampling technique, binge eating scale (BES) and clinical interviews, 30 persons were selected among all clients who had referred to weight loss centers in Qazvin, these persons were randomly placed into two control and experimental groups. The experimental group participated in a seven-session plan on short-term cognitive-behavioral group therapy. Results: The results showed that the short term group cognitive-behavioral therapy results in a significant reduction in binge eating signs and depressive symptoms within the experimental group, compared to the control. Conclusion: Regarding the results, it is known that short-term group cognitive-behavioral therapy is effective in reducing overeating symptoms. Hence, it can be used as an economical and effective treatment method for individuals suffering from BED