Human–Machine Interface of Rehabilitation Exoskeletons with Redundant Electromyographic Channels

Trifonov, A A; Filist, S. A.; Degtyarev, S. V.; Serebrovsky, V. V.; Шаталова, О. В.

doi:10.1007/978-981-15-5580-0_19

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…Therefore, how to make the exoskeletons work as flexibly as a real limb is now a key point [3][4][5]. The sEMG was a reliable way that has been widely applied to rehabilitation assessment [6,7], human intention prediction [8][9][10], human motion classification [11][12][13], prosthesis control [14], and rehabilitation robot control [15][16][17][18][19]. Miao et al [20] presented a platform robotics system with a subject-specific workspace for bilateral rehabilitation training.…”

Section: Introductionmentioning

confidence: 99%

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

Guo

2022

Life

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The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the mAP@0.5 could reach 82.3%, and mAP@0.5–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.

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Section: Introductionmentioning

confidence: 99%

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

Guo

2022

Life

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Rehabilitation Process to Control and Predict User Hand Gestures Through Emg Signal-Based Reinforced Transradial Amputation Model

SURYA,

RAMAMOORTHY

2024

J. Mech. Med. Biol.

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Assistive devices support disabled people through object traction intention and prediction. To develop a smart assistive device to support paralyzed patients through the flexible nature of gloves which allow the user finger movements which has no strength or nerve-based controls. The kinematic signal variations at the upper limb position are collected through the electromyography (EMG) signals. The interaction between the prosthetic hand and the human-disabled part is achieved through inferred parameters used in the communication. The rehabilitation and assistive technologies enhance the process of user intention to perform the task under various situations. The intended object prediction model involves human interaction with prosthetic signals. The object tracking and hand movement positions are combined in the proposed model to predict the exact activities of the user. The low and high muscle variations derive the pattern and its associated task. The proposed model introduces the deep transradial amputation model (DTAM) to predict the user intention movement based on EMG sensor-based hand gesture recognition. The proposed method analyzes the EMG signals collected from the upper and lower hand muscles. The model also constructs and trains the data to predict the 3D hand gestures and their positions from the features collected through EMG signals. The reinforcement-based recurrent fuzzy neural network (RFNN) is used to derive the pattern by combining various positions of the hand gesture. The maximum reward value used to obtain the accurate prediction is a performance metric. The correlation mapping and its coefficient values provide sufficient evidence to analyze the muscle variation data to predict user-intended activities. The 3D prosthetic hand values and finger positions of the complex object task acceleration can be predicted with the mean performance of [Formula: see text] and NRMSE value = 0.101. The maximum reward count to 50 under the various iteration processes to analyze the movements. The proposed transradial amputation manages to predict the user’s intention within the time period of 124[Formula: see text]ms. Through the results, the model enhances the task intention prediction and movement position quickly compared to the other models.

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Hybrid Method for Controlling Muscle Fatigue in the Robotic System

Kuzmin,

Tomakova,

Petrunina

et al. 2024

jour

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The purpose of research is development of a method for controlling muscle fatigue in robotic devices operating in a combined mode.Methods. To calculate the exogenous moment of forces of a robotic device, a surface electromyosignal decoder is proposed, which takes into account the effect of the operator's muscle fatigue. By decoding the electromyosignal, the assisting torque on the servomotors of the robotic device is determined. When calculating the assisting moment, the degree of muscle fatigue is taken into account. The method for assessing muscle fatigue consists in assessing the indicator of synchronism of electromyosignals on synergistic muscles and is based on a hybrid approach to the formation of a decision-making module. The first decision-making module is built on the basis of a neural network classifier, the descriptors for which are formed based on the analysis of the spectra of electromyosignals of synergistic muscles. The second decision module includes two synergy channels per electromyographic channel. The first synergy channel is obtained by amplitude demodulation of the electromyosignal, and the second - by its frequency demodulation. As a result, we obtain two muscle fatigue classifiers, the solutions of which are integrated by the aggregator.Results. Experimental studies of the dependence of the electromyosignal on the magnitude of muscle effort and its duration were carried out, which showed that the relative change in the average RMS index under static load can serve as an objective indicator of the degree of muscle fatigue.Conclusion. The developed method makes it possible to control the mechanical moments on the servomotors of a robotic device adequately to the test muscle load and the functional state of the user's muscles. The method allows for individual adjustment of the neural network classifier block and the fuzzy inference block with subsequent aggregation of their solutions and thus optimize the combined operation mode of the robotic device.

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Human–Machine Interface of Rehabilitation Exoskeletons with Redundant Electromyographic Channels

Cited by 6 publications

References 3 publications

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

Rehabilitation Process to Control and Predict User Hand Gestures Through Emg Signal-Based Reinforced Transradial Amputation Model

Hybrid Method for Controlling Muscle Fatigue in the Robotic System

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