Latent Factors Limiting the Performance of sEMG-Interfaces

Lobov, Sergey; Krilova, Nadia; Kastalskiy, Innokentiy; Kazantsev, Victor B.; Makarov, Valeri A.

doi:10.20944/preprints201804.0044.v1

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…In recent years, there have been various initiatives to publish datasets of EMG signals and pose estimations (Atzori et al, 2014;Lobov et al, 2018;Jarque-Bou et al, 2019;Pradhan et al, 2022) aiming to provide the essential data required for improving machine learning models, myoelectric prostheses control, and, ultimately, restoring natural hand function for people with upperlimb disabilities. Typically, these datasets use either a clinical or consumer-grade EMG signals acquisition system with 8-12 EMG electrodes and a Cyberglove for hand motion capture.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Simar,

Colot,

Cebolla

et al. 2024

Front. Neurosci.

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Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the “move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

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Section: Discussionmentioning

confidence: 99%

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Simar,

Colot,

Cebolla

et al. 2024

Front. Neurosci.

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“…In future, we plan to extend the research with more participants, compared with traditional in neurorehabilitation domain monophasic and biphasic square pulses stimulation for both agonist and antagonist muscles. The authors suppose that there is an interesting option to close the loop taking into account the EMG as the feedback loop to set up the electrical stimulation pattern formation and use the estimate of muscle reciprocity with the method described in Lobov et al (2018).…”

Section: Discussionmentioning

confidence: 99%

Neurointerface with oscillator motifs for inhibitory effect over antagonist muscles

Mikhailova

Pozdeeva

Suleimanova³

et al. 2023

Front. Neurosci.

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The effect of inhibitory management is usually underestimated in artificial control systems, using biological analogy. According to our hypothesis, the muscle hypertonus could be effectively compensated via stimulation by bio-plausible patterns. We proposed an approach for the compensatory stimulation device as implementation of previously presented architecture of the neurointerface, where (1) the neuroport is implemented as a DAC and stimulator, (2) neuroterminal is used for neurosimulation of a set of oscillator motifs on one-board computer. In the set of experiments with five volunteers, we measured the efficacy of motor neuron inhibition via the antagonist muscle or nerve stimulation registering muscle force with and without antagonist stimulation. For the agonist activation, we used both voluntary activity and electrical stimulation. In the case of stimulation of both the agonist and the antagonist muscles and nerves, we experimented with delays between muscle stimulation in the range of 0–20 ms. We registered the subjective discomfort rate. We did not identify any significant difference between the antagonist muscle and nerve stimulation in both voluntary activity and electrical stimulation of cases showing agonist activity. We determined the most effective delay between the stimulation of the agonist and the antagonist muscles and nerves as 10–20 ms.

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“…A representative EMG dataset for motion intent with hand gestures in float32 data format was used for this study [56]. To classify motion intent for prosthetics, a simple feed-forward neural network (FFNN) with one hidden layer was chosen.…”

Section: Network and Emg Datasetmentioning

confidence: 99%

Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

Just,

Ghinami,

Zbinden

et al. 2024

IEEE Access

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Motion intent recognition for controlling prosthetic systems has long relied on machine learning algorithms. Artificial neural networks have shown great promise for solving such nonlinear classification tasks, making them a viable method for this purpose. To bring these advanced methods and algorithms beyond the confines of the laboratory and into the daily lives of prosthetic users, self-contained embedded systems are essential. However, embedded systems face constraints in size, computational power, memory footprint, and power consumption, as they must be non-intrusive and discreetly integrated into commercial prosthetic components. One promising approach to tackle these challenges is to use network quantization, which allows complying with limitations without significant loss in accuracy. Here, we compare network quantization performance for self-contained systems using TensorFlow Lite and the recently developed QKeras platform. Due to internal libraries, the use of TensorFlow Lite led to a 8 times higher flash memory usage than that of the unquantized reference network, disadvantageous for self-contained prosthetic systems. In response, we offer open-source code solutions that leverage the QKeras platform, effectively reducing flash memory requirements by 24 times compared to Tensorflow Lite. Additionally, we conducted a comprehensive comparison of state-of-the-art microcontrollers. Our results reveal that the adoption of new architectures offers substantial reductions in inference time and power consumption. These improvements pave the way for real-time decoding of motor intent using more advanced machine learning algorithms for daily life usage, possibly enabling more reliable and precise control for prosthetic users.

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Latent Factors Limiting the Performance of sEMG-Interfaces

Cited by 10 publications

References 26 publications

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Neurointerface with oscillator motifs for inhibitory effect over antagonist muscles

Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

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