Precise Control Method on Prosthetic Hand Using sEMG Signals

Orban, Mostafa; Zhang, Xiaodong; Lu, Zhufeng; Marcal, Antonio; Emad, Ahmed; Masengo, Gilbert

doi:10.1109/cyber50695.2020.9279179

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Five hand motions were classified based on the sEMG signals recorded by their electrodes as well as commercially available wet rigid electrodes. Orban et al [13] used a prosthetic hand precise control method to control the position of the prosthetic hand finger's motion depending on the sEMG signals as the control command; the controller used two different tuning methods genetic algorithm and Ziegler-Nichols. Tsinganos et al [14] utilized the Hilbert space-filling curve for the generation of image representations of sEMG signals that were then classified by convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Han

Zhao²,

Wang³

et al. 2021

Mobile Information Systems

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The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

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Section: Related Workmentioning

confidence: 99%

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Han

Zhao²,

Wang³

et al. 2021

Mobile Information Systems

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“…One of the most common injuries in stroke patients is the loss of wrist motion, a crucial joint connecting the hand and the arm. As such, rehabilitation training for patients with stroke and wrist injuries is essential for improving their overall health and well-being [2,3]. With the help of specialized rehabilitation techniques and assistive devices, stroke patients can regain some of their motor abilities and improve their quality of life.…”

Section: Introductionmentioning

confidence: 99%

Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System

Guo

Orban

et al. 2023

Bioengineering

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Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient’s physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution—a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4–5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove’s effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm (the 1D-CNN algorithm and the InceptionTime algorithm). The InceptionTime algorithm classified ten hand gestures’ sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke’s physical, financial, and social impact.

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Advancements in Prosthetic Hand Control: An IoT-Convolutional Neural Network Fusion for Gesture Recognition

Dhanalakshmi,

Hemalatha,

et al. 2023

2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)

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Precise Control Method on Prosthetic Hand Using sEMG Signals

Cited by 3 publications

References 10 publications

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System

Advancements in Prosthetic Hand Control: An IoT-Convolutional Neural Network Fusion for Gesture Recognition

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