Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

Yang, Dapeng; Gu, Yikun; Thakor, Nitish V.; Liu, Hong

doi:10.1007/s00221-018-5441-x

Cited by 46 publications

(32 citation statements)

References 123 publications

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“…On the one hand of HMI, HDL shortens the training time with the help of layer-by-layer training and expert experience, but further research on the generalization performance is necessary to make the algorithm adapt to different individuals quickly [26,27] . On the other hand of sEMG controlled prosthesis, we conducted the preliminary test on upper limb amputee and found that the recognition sharply deteriorated before and after wearing the prosthetic hand, which is a difficult problem in the field of prosthetic research [28,29] . Therefore, we will further carry on how to improve the robustness of prosthetic control.…”

Section: Discussionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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In this paper an automated data labeling (ADL) neural network was proposed to streamline dataset collecting for real-time predicting the continuous motion of hand and wrist, these gestures are only decoded from a surface electromyography (sEMG) array of eight channels. Unlike collecting both the bio-signals and hand motion signals as samples and labels in supervised learning, this algorithm only collects the unlabeled sEMG into an unsupervised neural network, in which the hand motion labels are auto-generated. The coefficient of determination (r2) for three DOFs, i.e. wrist flex/extension, wrist pro/supination, hand open/close, was 0.86, 0.89 and 0.87 respectively. The comparison between real motion labels and auto-generated labels shows that the latter has earlier response than former. The results of Fitts' law test indicate that ADL has capability of controlling multi-DOFs simultaneously even though the training set only contains sEMG data from single DOF gesture. Moreover, no more hand motion measurement needed which greatly helps upper-limb amputee imagine the gesture of residual limb to control a dexterous prosthesis.

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Section: Discussionmentioning

confidence: 99%

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Zeng

Chen

et al. 2019

Preprint

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“…The features were extracted in windows of 300 ms with 100 ms overlap, in order to yield a high classification accuracy with a fast update time [47]. The linear discriminant analysis (LDA) was used for classification while multiple linear regression models (one per class) were applied to estimate muscle activation level, providing thereby sequential and proportional control along the two DoFs [34,[48][49][50][51].…”

Section: Signal Processing and Myoelectric Controlmentioning

confidence: 99%

A compact system for simultaneous stimulation and recording for closed-loop myoelectric control

Garenfeld

Jorgovanović

Ilić

et al. 2021

J NeuroEngineering Rehabil

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Background Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control. Methods We present a novel compact solution for simultaneous recording and stimulation through dynamic blanking of stimulation artefacts. To test the system, a feedback coding scheme communicating wrist rotation and hand aperture was developed specifically to stress the myoelectric control while still providing meaningful information to the subjects. Ten subjects participated in an experiment, where the quality of closed-loop myoelectric control was assessed by controlling a cursor in a two degrees of freedom target-reaching task. The benchmark performance with visual feedback was compared to that achieved by combining visual feedback and electrotactile stimulation as well as by using electrotactile feedback only. Results There was no significant difference in performance between visual and combined feedback condition with regards to successfully reached targets, time to reach a target, path efficiency and the number of overshoots. Therefore, the quality of myoelectric control was preserved in spite of the stimulation. As expected, the tactile condition was significantly poorer in completion rate (100/4% and 78/25% for combined and tactile condition, respectively) and time to reach a target (9/2 s and 13/4 s for combined and tactile condition, respectively). However, the performance in the tactile condition was still good, with no significant difference in path efficiency (38/8%) and the number of overshoots (0.5/0.4 overshoots), indicating that the stimulation was meaningful for the subjects and useful for closed-loop control. Conclusions Overall, the results demonstrated that the developed system can provide robust closed-loop control using electrotactile stimulation. The system supports different encoding schemes and allows placing the recording and stimulation electrodes next to each other. This is an important step towards an integrated solution where the developed unit will be embedded into a prosthetic socket.

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“…Most commercial systems are controlled using a two-channel direct and proportional control, which is effective and intuitive for a simple prosthesis (e.g., single degree of freedom gripper) [4]. For more advanced devices with several functions, machine learning can be used to extract multiple commands from the multichannel EMG [5], and indeed some solutions based on pattern classification are already commercially available (e.g., COAPT engineering [6] and MyoPlus from Otto Bock [7]).…”

Section: Introductionmentioning

confidence: 99%

Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

Boschmann

Neuhaus

Vogt

et al. 2021

J NeuroEngineering Rehabil

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Background Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training. Methods In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback. Results The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7). Conclusion The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.

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Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

Cited by 46 publications

References 123 publications

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

A compact system for simultaneous stimulation and recording for closed-loop myoelectric control

Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

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