Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review
The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable c...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-0825/14/1/16 |
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| author | Nafizul Alam Sk Hasan Gazi Abdullah Mashud Subodh Bhujel |
| author_facet | Nafizul Alam Sk Hasan Gazi Abdullah Mashud Subodh Bhujel |
| author_sort | Nafizul Alam |
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| description | The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications. |
| format | Article |
| id | doaj-art-ba92350835cb4e3a80a5c96f883bfb87 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-ba92350835cb4e3a80a5c96f883bfb872025-01-24T13:15:10ZengMDPI AGActuators2076-08252025-01-011411610.3390/act14010016Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic ReviewNafizul Alam0Sk Hasan1Gazi Abdullah Mashud2Subodh Bhujel3Department of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USADepartment of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USADepartment of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USADepartment of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USAThe integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications.https://www.mdpi.com/2076-0825/14/1/16CNNRNNLSTMsEMGRBFNNfuzzy and deep neural networks |
| spellingShingle | Nafizul Alam Sk Hasan Gazi Abdullah Mashud Subodh Bhujel Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review Actuators CNN RNN LSTM sEMG RBFNN fuzzy and deep neural networks |
| title | Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review |
| title_full | Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review |
| title_fullStr | Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review |
| title_full_unstemmed | Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review |
| title_short | Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review |
| title_sort | neural network for enhancing robot assisted rehabilitation a systematic review |
| topic | CNN RNN LSTM sEMG RBFNN fuzzy and deep neural networks |
| url | https://www.mdpi.com/2076-0825/14/1/16 |
| work_keys_str_mv | AT nafizulalam neuralnetworkforenhancingrobotassistedrehabilitationasystematicreview AT skhasan neuralnetworkforenhancingrobotassistedrehabilitationasystematicreview AT gaziabdullahmashud neuralnetworkforenhancingrobotassistedrehabilitationasystematicreview AT subodhbhujel neuralnetworkforenhancingrobotassistedrehabilitationasystematicreview |