Long short-term memory (LSTM) recurrent neural network for muscle activity detection
Published 2021 View Full Article
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Title
Long short-term memory (LSTM) recurrent neural network for muscle activity detection
Authors
Keywords
-
Journal
Journal of NeuroEngineering and Rehabilitation
Volume 18, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-10-22
DOI
10.1186/s12984-021-00945-w
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Note: Only part of the references are listed.- Deep Learning for EMG-based Human-Machine Interaction: A Review
- (2021) Dezhen Xiong et al. IEEE-CAA Journal of Automatica Sinica
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- (2021) Néstor J. Jarque-Bou et al. SENSORS
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- (2020) Marco Ghislieri et al. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
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- (2020) Zhen Zhang et al. SENSORS
- The Use of Wearable Sensors for the Movement Assessment on Muscle Contraction Sequences in Post-Stroke Patients during Sit-to-Stand
- (2019) Wei-Chun Hsu et al. SENSORS
- Asymmetry Index in Muscle Activations
- (2019) C. Castagneri et al. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
- Neural muscle activation detection: A deep learning approach using surface electromyography
- (2019) Iman Akef Khowailed et al. JOURNAL OF BIOMECHANICS
- EMG-based online classification of gestures with recurrent neural networks
- (2019) Miguel Simão et al. PATTERN RECOGNITION LETTERS
- A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
- (2018) Yu Hu et al. PLoS One
- EMG Pattern Classification by Split and Merge Deep Belief Network
- (2016) Hyeon-min Shim et al. Symmetry-Basel
- Instrumented Gait Analysis for an Objective Pre-/Postassessment of Tap Test in Normal Pressure Hydrocephalus
- (2015) Valentina Agostini et al. ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
- Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience
- (2015) Hyeon-min Shim et al. Journal of Central South University
- Characterizing EMG data using machine-learning tools
- (2014) Jamileh Yousefi et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Subspace based adaptive denoising of surface EMG from neurological injury patients
- (2014) Jie Liu et al. Journal of Neural Engineering
- Gait Parameters and Muscle Activation Patterns at 3, 6 and 12 Months After Total Hip Arthroplasty
- (2013) Valentina Agostini et al. JOURNAL OF ARTHROPLASTY
- The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients
- (2013) Xu Zhang et al. Journal of Neural Engineering
- Novel formulation of a double threshold algorithm for the estimation of muscle activation intervals designed for variable SNR environments
- (2012) G. Severini et al. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
- Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes
- (2012) Xu Zhang et al. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
- An Algorithm for the Estimation of the Signal-To-Noise Ratio in Surface Myoelectric Signals Generated During Cyclic Movements
- (2011) V. Agostini et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Normative EMG activation patterns of school-age children during gait
- (2010) V. Agostini et al. GAIT & POSTURE
- Filtering the surface EMG signal: Movement artifact and baseline noise contamination
- (2010) Carlo J. De Luca et al. JOURNAL OF BIOMECHANICS
- Automatic detection of surface EMG activation timing using a wavelet transform based method
- (2010) Giuseppe Vannozzi et al. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY
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