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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

Manfredo Atzori, Matteo Cognolato, Henning Müller
2016 Frontiers in Neurorobotics  
350 words) 9 P r o v i s i o n a l Deep learning electromyography 2 This is a provisional file, not the final typeset article P r o v i s i o n a l  ...  Thus, it seems reasonable to investigate 114 its abilities in surface electromyography as well. 115 Despite it often being considered a new and emerging field, the birth of deep learning can be set in  ...  ; Hinton et al., 2012) 134 Deep learning methods are also successfully applied to applications requiring the process of big 135 amount of data, such as drug discovery (Ramsundar et al., 2015), compound  ... 
doi:10.3389/fnbot.2016.00009 pmid:27656140 pmcid:PMC5013051 fatcat:oumpk6uq2vfqzm7stb3lawz6oi

Application of digital signal processing and machine learning for Electromyography: A review

Siti Nashayu Omar
2021 Asian Journal Of Medical Technology  
Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.  ...  This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies.  ...  Machine learning algorithms, such as traditional machine learning algorithms, and reinforcement learning algorithms, have been widely used in the medical field and have played an important role in the  ... 
doi:10.32896/ajmedtech.v1n1.30-45 fatcat:r556h7pp55apxb57l5fqnoq2z4

CLASSIFICATION OF AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY ELECTROMYOGRAPHY SIGNALS BASED ON TRANSFER LEARNING

Abdulkadir ŞENGÜR, Ümit BUDAK, Yaman AKBULUT
2018 European Journal of Technic  
This paper investigates the usage of transfer learning in amyotrophic lateral sclerosis (ALS) disease detection. ALS is a dangerous disease which affects the nerve cells in brain and spinal cord.  ...  The proposed work uses EMG signals in discrimination of the ALS and healthy persons.  ...  CNN and reinforcement sample learning strategy were then employed to classify these features. In this paper, a deep learning architecture is used for EMG signal classification.  ... 
doi:10.36222/ejt.498095 fatcat:xdwpkfvfbzfcbkxtj5kvqpho3e

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

R. Kanthavel, R. Dhaya
2022 Computer systems science and engineering  
This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning.  ...  Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle.  ...  The essential model of reinforcement learning model combines reinforcement deep learning, and can be applied to the medical environment [6] .  ... 
doi:10.32604/csse.2022.021606 fatcat:jeqs6kegw5hdxpisj2cpqcsq5e

Combining deep belief networks and bidirectional long short-term memory: Case study: Sleep stage classification

Intan Nurma Yulita, Mohamad Ivan Fanany, Aniati Murni Arymurthy
2017 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)  
The recording comes from electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG) represented in signal form.  ...  This paper proposes a new combination of Deep Belief Networks (DBN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for Sleep Stage Classification.  ...  ACKNOWLEDGMENT The Author thanks to the Indonesian Endowment Fund for Education (LPDP) and Machine Learning and Computer Vision Laboratory, Universitas Indonesia that contributed and supported the study  ... 
doi:10.1109/eecsi.2017.8239089 fatcat:u7ujq3tpsvbfraunil3iorlqqy

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features [article]

Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin, Erik Scheme
2019 arXiv   pre-print
Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition.  ...  However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features.  ...  Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands.  ... 
arXiv:1912.00283v1 fatcat:wjirojfedbho5ouu2nkmjpjyqq

WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

Aras R. Dargazany, Paolo Stegagno, Kunal Mankodiya
2018 Mobile Information Systems  
features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data.  ...  This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things  ...  Deep Supervised Learning (DSL), and Deep Reinforcement Learning (DRL).  ... 
doi:10.1155/2018/8125126 fatcat:ty3a7n4in5aahbqyl7wum5vonq

Deep Learning in Bioinformatics [article]

Seonwoo Min, Byunghan Lee, Sungroh Yoon
2016 arXiv   pre-print
Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry.  ...  We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.  ...  In this review, we provided an extensive review of bioinformatics research applying deep learning in terms of input data, research objectives, and the characteristics of established deep learning architectures  ... 
arXiv:1603.06430v5 fatcat:xvgg7misrrcsxmshty2emnujaq

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
2022 Sensors  
We also present cutting-edge frontiers and future directions for deep learning-based HAR.  ...  Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices.  ...  However, in recent years, due to huge advancements in the availability and computational power of computing resources and cutting-edge deep learning techniques, the applied deep learning area has been  ... 
doi:10.3390/s22041476 pmid:35214377 pmcid:PMC8879042 fatcat:vp6jssypezbd5cnyzn4g35eqrm

Deep learning approach to control of prosthetic hands with electromyography signals [article]

Mohsen Jafarzadeh, Daniel Curtiss Hussey, Yonas Tadesse
2019 arXiv   pre-print
In this paper, we propose a deep learning approach to control prosthetic hands with raw EMG signals. We use a novel deep convolutional neural network to eschew the feature-engineering step.  ...  The proposed approach is implemented in Python with TensorFlow deep learning library, and it runs in real-time in general-purpose graphics processing units of NVIDIA Jetson TX2 developer kit.  ...  ACKNOWLEDGMENT We would like to thank Cameron Ovandipour and Ngoc Tuyet Nguyen Yount for valuable contributions in developing this project. We would like to express our very great appreciation to Dr.  ... 
arXiv:1909.09910v1 fatcat:j5g6kir3gbelnh7lnww3xlyaqm

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances [article]

Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
2022 arXiv   pre-print
We also present cutting-edge frontiers and future directions for deep learning-based HAR.  ...  Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices.  ...  Deep Reinforcement Learning (DRL) AE, DBN, CNNs fall within the realm of supervised or unsupervised learning.  ... 
arXiv:2111.00418v5 fatcat:wylhzwkndjar7fc3esvhca2axi

Elderly Fall Detection Systems: A Literature Survey

Xueyi Wang, Joshua Ellul, George Azzopardi
2020 Frontiers in Robotics and AI  
We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy.  ...  In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT.  ...  Machine learning Machine learning Machine learning Deep learning Deep learning Deep learning Deep learning Reinforcement learning Reinforcement learning Reinforcement learning Reinforcement  ... 
doi:10.3389/frobt.2020.00071 pmid:33501238 pmcid:PMC7805655 fatcat:iredkfo5qra7pbmdkjy4fsftya

Analysis on Deep Learning methods for ECG based Cardiovascular Disease prediction

S Kusuma, J Divya Udayan
2020 Scalable Computing : Practice and Experience  
In this paper the applicationof deep learning methods for CVD diagnosis using ECG is addressed.A detailed Analysis of related articles has been conducted.  ...  This research paper looks into theadvantages of deep learning approaches that can be brought by developing aframework that can enhance prediction of heart related diseases using ECG.  ...  In most cases, researchers within the field of deep learning are focused on developing new data analysis methods.  ... 
doi:10.12694/scpe.v21i1.1640 fatcat:4tcuha4xmrgf7opu2u2t2ujkta

Detecting Human Driver Inattentive and Aggressive Driving Behavior using Deep Learning: Recent Advances, Requirements and Open Challenges

Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad
2020 IEEE Access  
After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction  ...  However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network  ...  Deep reinforcement learning (DRL) is a combination of Reinforcement learning and Deep Learning in which deep learning determines the action taken at every stage by creating a sequential reinforcement learning  ... 
doi:10.1109/access.2020.2999829 fatcat:5nxtzm6yfbe4jf6nqgreqw45r4

Artificial intelligence and machine learning in spine research

Fabio Galbusera, Gloria Casaroli, Tito Bassani
2019 JOR Spine  
Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being  ...  Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech  ...  As a matter of facts, most of the scientific papers applying AI to spine research, which are described in the paragraph "Applications of AI and ML in spine research," are based on deep learning.  ... 
doi:10.1002/jsp2.1044 pmid:31463458 pmcid:PMC6686793 fatcat:2ahy3rxwxrdbbgofqugbfiy4ry
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