Spatio-temporal warping for myoelectric control: an offline, feasibility study

Jabbari, Milad; Khushaba, Rami N.; Nazarpour, Kianoush

doi:10.1088/1741-2552/ac387f

Cited by 5 publications

(3 citation statements)

References 68 publications

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“…The raw myoelectric signals allow the experimenter to playback the muscle activity during at-home use and develop comprehensive datasets for further analysis, e.g. for machine learning-based decoding [40,41]. Meanwhile, transmitting the raw data will increase the resource consumption, in terms of bandwidth and storage space, but a large portion of them may not be used in scientific research due to the lack of contextual information [42].…”

Section: Discussionmentioning

confidence: 99%

Internet of Things for beyond-the-laboratory prosthetics research

Dyson

Nazarpour

2022

Phil. Trans. R. Soc. A.

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Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

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Section: Discussionmentioning

confidence: 99%

Internet of Things for beyond-the-laboratory prosthetics research

Dyson

Nazarpour

2022

Phil. Trans. R. Soc. A.

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“…Last, in Ref. 42 a spatio-temporal framework extended the well known Dynamic time wrapping (DTW) similarity to operate in a spatial setting and was combined with an LSTM model. Results were provided for four public datasets, including some from the NinaPro project, and were shown to be more accurate than other deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Ovadia,

Segal,

Rabin

2024

Sci Rep

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Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.

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“…Wavelet transform-based feature by Chu et al [11], waveletbased iterative feature, namely, ternary pattern by Turker et al [12], energy features by Karnam et al [13], and variational mode decomposition (VMD)-based feature extraction methods [14] were developed for efficient EMG classification. A spatiotemporal feature developed by Jabbari et al [15], namely spatiotemporal warping (STW), outperformed the traditional features.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Myoelectric Signal Classification Through Conditional Spectral Moments and Wavelet-Enhanced Time-Domain Descriptors

Murugiah,

William,

Mariapushpam

et al. 2024

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This study underscores the imperative role of feature extraction and classification in the domain of electromyography (EMG) signals. The efficacy of myoelectric pattern recognition hinges upon the judicious selection of pertinent features. In this research endeavor, we introduce a novel approach that leverages two distinct feature sets: conditional spectral moments and refined time-domain descriptors, tailored to augment the precision of myoelectric signal classification. The conditional spectral moments, derived from the timefrequency distribution of EMG signals, encapsulate nuanced variations in muscle activity and movement dynamics. This augmentation facilitates seamless differentiation of hand gestures, a pivotal advancement in the context of prosthetic applications. Concurrently, we enhance the time-domain descriptors by convolving them with wavelet filter coefficients, thereby extracting both spatial and temporal characteristics of muscle activity. The extracted features are subjected to classification using Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Ensemble Bagging classifiers. To elucidate the efficacy of our proposed features, comprehensive experiments are conducted employing the benchmark databases Ninapro DB1 (comprising 52 classes) and DB2 (comprising 49 classes). Our findings underscore the superiority of the proposed features, particularly when applied in conjunction with ensemble classifiers. Specifically, the classification accuracies of the modified time-domain descriptor feature exhibit substantial enhancements, achieving 87.1% and 85.3% accuracy rates for Ninapro DB1 and DB2, respectively. Notably, the conditional spectral moments outperform with remarkable classification accuracies of 92.9% and 90.8% for Ninapro DB1 and DB2, respectively. This marked improvement in EMG signal classification corroborates the enhanced precision in decoding hand movements, thus imparting significant advancements in the realm of hand prosthesis control.

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Spatio-temporal warping for myoelectric control: an offline, feasibility study

Cited by 5 publications

References 68 publications

Internet of Things for beyond-the-laboratory prosthetics research

Internet of Things for beyond-the-laboratory prosthetics research

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Enhancing Myoelectric Signal Classification Through Conditional Spectral Moments and Wavelet-Enhanced Time-Domain Descriptors

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