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Khantan M, Avery M, Aung PT, Zarin RM, Hammelef E, Shawki N, Serruya MD, Napoli A. The NuroSleeve, a user-centered 3D printed hybrid orthosis for individuals with upper extremity impairment. J Neuroeng Rehabil 2023; 20:103. [PMID: 37542335 PMCID: PMC10403889 DOI: 10.1186/s12984-023-01228-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND Active upper extremity (UE) assistive devices have the potential to restore independent functional movement in individuals with UE impairment due to neuromuscular diseases or injury-induced chronic weakness. Academically fabricated UE assistive devices are not usually optimized for activities of daily living (ADLs), whereas commercially available alternatives tend to lack flexibility in control and activation methods. Both options are typically difficult to don and doff and may be uncomfortable for extensive daily use due to their lack of personalization. To overcome these limitations, we have designed, developed, and clinically evaluated the NuroSleeve, an innovative user-centered UE hybrid orthosis. METHODS This study introduces the design, implementation, and clinical evaluation of the NuroSleeve, a user-centered hybrid device that incorporates a lightweight, easy to don and doff 3D-printed motorized UE orthosis and a functional electrical stimulation (FES) component. Our primary goals are to develop a customized hybrid device that individuals with UE neuromuscular impairment can use to perform ADLs and to evaluate the benefits of incorporating the device into occupational therapy sessions. The trial is designed as a prospective, open-label, single-cohort feasibility study of eight-week sessions combined with at-home use of the device and implements an iterative device design process where feedback from participants and therapists informs design improvement cycles. RESULTS All participants learned how to independently don, doff, and use the NuroSleeve in ADLs, both in clinical therapy and in their home environments. All participants showed improvements in their Canadian Occupational Performance Measure (COPM), which was the primary clinical trial outcome measure. Furthermore, participants and therapists provided valuable feedback to guide further development. CONCLUSIONS Our results from non-clinical testing and clinical evaluation demonstrate that the NuroSleeve has met feasibility and safety goals and effectively improved independent voluntary function during ADLs. The study's encouraging preliminary findings indicate that the NuroSleeve has met its technical and clinical objectives while improving upon the limitations of the existing UE orthoses owing to its personalized and flexible approach to hardware and firmware design. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT04798378, https://clinicaltrials.gov/ct2/show/NCT04798378 , date of registration: March 15, 2021.
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Affiliation(s)
- Mehdi Khantan
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
- Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, 19121, USA
| | | | - Phyo Thuta Aung
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Rachel M Zarin
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Emma Hammelef
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Nabila Shawki
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Mijail Demian Serruya
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Alessandro Napoli
- Raphael Center for Neurorestoration, Thomas Jefferson University, Philadelphia, PA, 19107, USA.
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Wang S, Huang L, Jiang D, Sun Y, Jiang G, Li J, Zou C, Fan H, Xie Y, Xiong H, Chen B. Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition. Front Bioeng Biotechnol 2022; 10:909023. [PMID: 35747495 PMCID: PMC9209772 DOI: 10.3389/fbioe.2022.909023] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.
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Affiliation(s)
- Shudi Wang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Li Huang
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Li Huang, ; Du Jiang, ; Ying Sun, ; Guozhang Jiang, ; Baojia Chen,
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Li Huang, ; Du Jiang, ; Ying Sun, ; Guozhang Jiang, ; Baojia Chen,
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Li Huang, ; Du Jiang, ; Ying Sun, ; Guozhang Jiang, ; Baojia Chen,
| | - Guozhang Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Li Huang, ; Du Jiang, ; Ying Sun, ; Guozhang Jiang, ; Baojia Chen,
| | - Jun Li
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Cejing Zou
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Hanwen Fan
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Yuanmin Xie
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Hegen Xiong
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang, China
- *Correspondence: Li Huang, ; Du Jiang, ; Ying Sun, ; Guozhang Jiang, ; Baojia Chen,
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Yang Z, Jiang D, Sun Y, Tao B, Tong X, Jiang G, Xu M, Yun J, Liu Y, Chen B, Kong J. Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network. Front Bioeng Biotechnol 2021; 9:779353. [PMID: 34746114 PMCID: PMC8569623 DOI: 10.3389/fbioe.2021.779353] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 10/04/2021] [Indexed: 11/13/2022] Open
Abstract
Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.
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Affiliation(s)
- Zhiwen Yang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.,Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Guozhang Jiang
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Manman Xu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.,Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Juntong Yun
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Liu
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, Three Gorges University, Yichang, China
| | - Jianyi Kong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.,Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, China
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