Triggering Robot Hand Reflexes with Human EMG Data Using Spiking Neurons

Tieck, J. Camilo Vasquez; Weber, Sandro; Stewart, Terrence C.; Roennau, Arne; Dillmann, Rüdiger

doi:10.1007/978-3-030-01370-7_70

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…After that, the activation signal is used to trigger the oscillator to generate motor commands for motion. Therefore, the muscle activity is directly mapped to the prosthetic hand kinematics (Pani et al, 2017 ; Vasquez Tieck et al, 2019 ).…”

Section: Hand Gesture Recognition Challengesmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

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Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

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Section: Hand Gesture Recognition Challengesmentioning

confidence: 99%

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Shi

2021

Front. Neurosci.

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“…Spiking neural network approaches are being actively researched for healthcare applications as they promise low-power implementation critical for the application (Donati et al, 2018(Donati et al, , 2019Vasquez Tieck et al, 2019;Bezugam et al, 2022). Considering diagnostics of medical images, the significance of continual learning grows due to differences in imaging parameters and physiological changes in the data (Hofmanninger et al, 2020;Amrollahi et al, 2022).…”

Section: Healthcarementioning

confidence: 99%

A survey and perspective on neuromorphic continual learning systems

Mishra

2023

Front. Neurosci.

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With the advent of low-power neuromorphic computing systems, new possibilities have emerged for deployment in various sectors, like healthcare and transport, that require intelligent autonomous applications. These applications require reliable low-power solutions for sequentially adapting to new relevant data without loss of learning. Neuromorphic systems are inherently inspired by biological neural networks that have the potential to offer an efficient solution toward the feat of continual learning. With increasing attention in this area, we present a first comprehensive review of state-of-the-art neuromorphic continual learning (NCL) paradigms. The significance of our study is multi-fold. We summarize the recent progress and propose a plausible roadmap for developing end-to-end NCL systems. We also attempt to identify the gap between research and the real-world deployment of NCL systems in multiple applications. We do so by assessing the recent contributions in neuromorphic continual learning at multiple levels—applications, algorithms, architectures, and hardware. We discuss the relevance of NCL systems and draw out application-specific requisites. We analyze the biological underpinnings that are used for acquiring high-level performance. At the hardware level, we assess the ability of the current neuromorphic platforms and emerging nano-device-based architectures to support these algorithms in the presence of several constraints. Further, we propose refinements to continual learning metrics for applying them to NCL systems. Finally, the review identifies gaps and possible solutions that are not yet focused upon for deploying application-specific NCL systems in real-life scenarios.

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“…• UR3 [105] • Vector [106] • iRobot [107] • Khepera IV [30] • iCub [101] • Schunk SVH [108][109] [110] • sMEG sensor, Myo sensor [109][110]…”

Section: Soundmanmentioning

confidence: 99%

“…The trained network can classify the sEMG signals and detect finger activation. The reflexes of the finger are modelled with motion primitives and mapped to a robot kinematics [109] [110].…”

Section: Motor Control Applicationsmentioning

confidence: 99%

Neuromorphic Computing for Interactive Robotics: A Systematic Review

2022

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Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics. We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots.

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Triggering Robot Hand Reflexes with Human EMG Data Using Spiking Neurons

Cited by 7 publications

References 11 publications

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

A survey and perspective on neuromorphic continual learning systems

Neuromorphic Computing for Interactive Robotics: A Systematic Review

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