Development of an upper-limb neuroprosthesis to voluntarily control elbow and hand

Ogiri, Yosuke; Yamanoi, Yusuke; Nishino, Wataru; Kato, Ryu; Takagi, Tasuku; Yokoi, Hiroshi

doi:10.1080/01691864.2018.1507836

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Information on the able-bodied and amputee subjects is shown in Tables 1 and 2, respectively. Eight sEMG electrodes were arranged equidistant and parallel from each other around the upper arms of both types of subjects to ensure that the electrodes covered the major muscles of the subjects' biceps, triceps, and brachialis muscles while capturing as many valid sEMG signals as possible [23,24]. The skin on the upper arm of the subjects was treated with hair clipping, removal of dead skin and alcohol cleansing to ensure the stable acquisition of the surface sEMG signals.…”

Section: Data Acquisitionmentioning

confidence: 99%

Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation

Li,

Sun,

et al. 2024

Applied Sciences

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Pattern recognition in myoelectric control that relies on the myoelectric activity associated with arm motions is an effective control method applied to myoelectric prostheses. Individuals with transhumeral amputation face significant challenges in effectively controlling their prosthetics, as muscle activation varies with changes in arm positions, leading to a notable decrease in the accuracy of motion pattern recognition and consequently resulting in a high rejection rate of prosthetic devices. Therefore, to achieve high accuracy and arm position stability in upper-arm motion recognition, we propose a Deep Adversarial Inception Domain Adaptation (DAIDA) based on the Inception feature module to enhance the generalization ability of the model. Surface electromyography (sEMG) signals were collected from 10 healthy subjects and two transhumeral amputees while performing hand, wrist, and elbow motions at three arm positions. The recognition performance of different feature modules was compared, and ultimately, accurate recognition of upper-arm motions was achieved using the Inception C module with a recognition accuracy of 90.70% ± 9.27%. Subsequently, validation was performed using data from different arm positions as source and target domains, and the results showed that compared to the direct use of a convolutional neural network (CNN), the recognition accuracy on untrained arm positions increased by 75.71% (p < 0.05), with a recognition accuracy of 91.25% ± 6.59%. Similarly, in testing scenarios involving multiple arm positions, there was a significant improvement in recognition accuracy, with recognition accuracy exceeding 90% for both healthy subjects and transhumeral amputees.

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Section: Data Acquisitionmentioning

confidence: 99%

Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation

Li,

Sun,

et al. 2024

Applied Sciences

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“…One of the products of this prominent project is the SSSA-MyHand [ 5 ]. Meanwhile, our group has been developing multi-DOF myoelectric hands for several years now [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Learning Data Correction for Myoelectric Hand Based on “Survival of the Fittest”

et al. 2021

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In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by “survival of the fittest.” The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.

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Prospect of data science and artificial intelligence for patient-specific neuroprostheses

Yalug

Arslan

Öztürk-Işık

2021

Somatosensory Feedback for Neuroprosthetics

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Development of an upper-limb neuroprosthesis to voluntarily control elbow and hand

Cited by 4 publications

References 8 publications

Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation

Enhancing Robustness of Surface Electromyography Pattern Recognition at Different Arm Positions for Transhumeral Amputees Using Deep Adversarial Inception Domain Adaptation

Learning Data Correction for Myoelectric Hand Based on “Survival of the Fittest”

Prospect of data science and artificial intelligence for patient-specific neuroprostheses

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