LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control

Eddy, Ethan; Campbell, Evan; Phinyomark, Angkoon; Bateman, Scott; Scheme, Erik

doi:10.1109/access.2023.3304544

Cited by 6 publications

(4 citation statements)

References 86 publications

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“…Correspondingly, the RMS feature (see equation 1) was chosen in this work. All data processing was completed using LibEMG, an opensource library for myoelectric control [45].…”

Section: Data Processingmentioning

confidence: 99%

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Eddy,

Campbell,

Bateman

et al. 2024

J. Neural Eng.

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Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to ``confounding factors'' (i.e., factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1) limb position variability, (2) cross-day use, and a newly identified confound faced by discrete systems (3) gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

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“…Correspondingly, the RMS feature (see equation 1) was chosen in this work. All data processing was completed using LibEMG, an opensource library for myoelectric control [45].…”

Section: Data Processingmentioning

confidence: 99%

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Eddy,

Campbell,

Bateman

et al. 2024

J. Neural Eng.

Self Cite

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“…(1) where µ c,i is the mean of class c after adaptation, µ c,i−1 is the current mean of class c, μc is the mean of class c of the batch, a is the adaptation rate, α is the static adaptation rate, N b,c is the number of new samples of class c, and N c is the total number of samples in class c collected for adaptation thus far. * Note that this work was built with LibEMG, an opensource Python library for designing and evaluating realtime myoelectric control systems [44]. The code and data can be found at github.com/ECEEvanCampbell/ CIIL_LDA.…”

Section: Control Schemementioning

confidence: 99%

“…This work required constant communication between different processes performing user-in-the-loop classification, collecting the associated context, and periodically performing adaptation. We aim to provide the supporting infrastructure for this style of experiment in LibEMG in the future [44].…”

Section: Future Workmentioning

confidence: 99%

Context-informed incremental learning improves both the performance and resilience of myoelectric control

Campbell,

Eddy,

Bateman

et al. 2024

J NeuroEngineering Rehabil

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Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model’s parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

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“…Furthermore, as our research prioritizes CNN for ease of hardware implementation, we acknowledge recent studies indicating that traditional ML feature extraction methods can effectively compete with and overcome the limitations of CNN [36], [37]. Therefore, in our next phase of research, we will conduct comparative studies between these two approaches and explore the deployment of ML on edge devices for practical performance in real-world applications.…”

Section: Limitations and Feature Workmentioning

confidence: 99%

FPGA-based Lightweight QDS-CNN System for sEMG Gesture and Force Level Recognition

Guo,

Gou,

Yao

et al. 2024

IEEE Trans. Biomed. Circuits Syst.

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Deep learning (DL) has been used for electromyographic (EMG) signal recognition and achieved high accuracy for multiple classification tasks. However, implementation in resourceconstrained prostheses and human-computer interaction devices remains challenging. To overcome these problems, this paper implemented a low-power system for EMG gesture and force level recognition using Zynq architecture. Firstly, a lightweight network model structure was proposed by Ultra-lightweight depth separable convolution (UL-DSC) and channel attention-global average pooling (CA-GAP) to reduce the computational complexity while maintaining accuracy. A wearable EMG acquisition device for real-time data acquisition was subsequently developed with size of 36 mm×28 mm×4 mm. Finally, a highly parallelized dedicated hardware accelerator architecture was designed for inference computation. 18 gestures were tested, including force levels from 22

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LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control

Cited by 6 publications

References 86 publications

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition

Context-informed incremental learning improves both the performance and resilience of myoelectric control

FPGA-based Lightweight QDS-CNN System for sEMG Gesture and Force Level Recognition

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