A General Arthropod Joint Model and its Applications in Modeling Human Robotic Joints

Liang, Renghao; Xu, Guanghua; Teng, Zhicheng; Li, Min; Zhang, Sicong; Zheng, Xiaowei

doi:10.1109/access.2021.3049469

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…In this study, a NeusenW-64 wireless EEG acquisition system (Neuracle Company, Beijing, China) was used to collect EEG signals. An exoskeleton hand system developed by Liang et al [35,36] was used to give actual kinesthetic feedback to subjects at the end of every trial, as shown in figure 1. We used Matlab 2020b (The MathWorks Inc., Natick, MA, USA) to process the EEG data and a Matlab open-source toolbox Psychtoolbox-3 (The MathWorks Inc.) was used to show experimental visual instruction, hand grasp video, and ErrP stimulus.…”

Section: Devicementioning

confidence: 99%

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Tao¹,

Jia²,

Xu³

et al. 2023

J. Neural Eng.

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Objective. Motor Imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI). Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments. Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization (ERD) modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after 3 MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after 6 experiments. Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only 3 training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.

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Section: Devicementioning

confidence: 99%

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Tao¹,

Jia²,

Xu³

et al. 2023

J. Neural Eng.

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“…Studies have progressed from initial observations of joint angles using 2-D images [12] to more comprehensive multijoint motion analyses through 3-D videos [13], [14]. Subsequently, wearable multi-angle sensors [15] and motion capture systems [16], [17] have been employed to determine the angular correlations among individual joints and the motion trajectory of the finger's tip. However, it's worth noting that the aforementioned research did not account for the position of the finger joint's center of rotation (COR).…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients

Yu,

Luo,

Zhu

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Wearing robotic gloves has become increas-1 ingly crucial for hand rehabilitation in stroke patients. 2 However, traditional robotic gloves can exert additional 3 pressure on the hand, such as prolonged use leading to 4 poor blood circulation and muscle stiffness. To address 5 these concerns, this work analyzes the finger kinematic 6 model based on computerized tomography (CT) images of 7 human hands, and designs a low-pressure robotic glove 8 that conforms to finger kinematic characteristics. Firstly, 9 physiological data on finger joint flexion and extension 10 were collected through CT scans. The equivalent rotation 11 centers of finger joints were obtained using the SURF and 12 RANSAC algorithms. Furthermore, the trajectory of finger 13 joint end and the correlation equation of finger joint motion 14 were fitted, and a comprehensive finger kinematic model 15 was established. Based on this finger kinematic model, 16 a novel under-actuated exoskeleton mechanism was de-17 signed using a human-machine integration approach. The 18 novel robotic glove fully aligns with the equivalent rota-19 tion centers and natural motion trajectories of the fingers, 20 exerting minimal and evenly distributed dynamic pressure 21 on the fingers, with a theoretical static pressure value 22 of zero. Experiments involving gripping everyday objects 23 demonstrated that the novel robotic glove significantly re-24 duces the overall pressure on the fingers during grasping 25 compared to the pneumatic glove and the traditional ex-26 oskeleton robotic glove. It is suitable for long-term use by 27 stroke patients for rehabilitation training.

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“…The proposed involute joint was inspired by the most common grasping gesture of the human hand-the power grip. From an evolutionary perspective, the human hand has adapted to grasp cylindrical objects in their natural state, as the trajectory of the fingertip presents a circular involute-shaped curve in its relaxed state [19][20][21][22][23][24]. Compared with n-bar linkage exoskeletons, the proposed mechanism can provide a required output trajectory to fit the motion of human fingertips in an efficient way (fewer kinematic pairs) (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Design and Characterization of a Rolling-Contact Involute Joint and Its Applications in Finger Exoskeletons

Liang

Zhang

et al. 2022

Machines

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The hand exoskeleton has been widely studied in the fields of hand rehabilitation and grasping assistance tasks. Current hand exoskeletons face challenges in combining a user-friendly design with a lightweight structure and accurate modeling of hand motion. In this study, we developed a finger exoskeleton with a rolling contact involute joint. Specific implementation methods were investigated, including an analysis of the mechanical characteristics of the involute joint model, the formula derivation of the joint parameter optimization algorithm, and the design process for a finger exoskeleton with an involute joint. Experiments were conducted using a finger exoskeleton prototype to evaluate the output trajectory and grasping force of the finger exoskeleton. An EMG-controlled hand exoskeleton was developed to verify the wearability and functionality of the glove. The experimental results show that the proposed involute joint can provide sufficient fingertip force (10N) while forming a lightweight exoskeleton to assist users with functional hand rehabilitation and grasping activities.

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A General Arthropod Joint Model and its Applications in Modeling Human Robotic Joints

Cited by 13 publications

References 24 publications

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients

Design and Characterization of a Rolling-Contact Involute Joint and Its Applications in Finger Exoskeletons

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