Feature extraction and selection for myoelectric control based on wearable EMG sensors

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p < 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems

Navigating features: A topologically informed chart of electromyographic features space

The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification problem on different datasets can display variations in the respective optimal sets, casting doubts on the generalizability of relevant features. Here, we approach this problem by leveraging topological tools to create charts of features spaces. These charts highlight feature sub-groups that encode similar information (and their respective similarities) allowing for a principled and interpretable choice of features for classification and analysis. Using multiple electromyographic (EMG) datasets as a case study, we use this feature chart to identify functional groups among 58 state-of-the-art EMG features, and to show that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions. We find that these groups describe meaningful non-redundant information, succinctly recapitulating information about different regions of feature space. We then recommend representative features from each group based on maximum class separability, robustness and minimum complexity

A Multi-Dataset Characterization of Window-based Hyperparameters for Deep CNN-driven sEMG Pattern Recognition

The control performance of myoelectric prostheses would not only depend on the feature extraction and classification algorithms but also on interactions of dynamic window-based hyper-parameters (WBHP) used to construct input signals. However, the relationship between these hyper-parameters and how they influence the performance of the convolutional neural networks (CNNs) during motor intent decoding has not been studied. Therefore, we investigated the impact of various combinations of WBHP (window length and overlap) employed for the construction of raw 2-dimensional (2D) surface electromyogram signals on the performance of CNNs when used for motion intent decoding. Moreover, we examined the relationship between the window length of the 2D sEMG and three commonly used CNN kernel sizes. To ensure high confidence in the findings, we implemented three CNNs which are variants of the existing models, and a newly proposed CNN model. Experimental analysis was conducted using three distinct benchmark databases, two from upper limb amputees and one from able-bodied subjects. The results demonstrate that the performance of the CNNs improved as the overlap between consecutively generated 2D signals increased, with 75% overlap yielding the optimal improvement by 12.62% accuracy and 39.60% F1-score compared to no overlap. Moreover, the CNNs performance was better for kernel size of seven than three and five across the databases. For the first time, we have established with multiple evidence that WBHP would substantially impact the decoding outcome and computational complexity of deep neural networks, and we anticipate that this may spur positive advancement in myoelectric control and related fields

Refined clothespin relocation test and assessment of motion

Background: Advancements in upper limb prosthesis design have focused on providing increased degrees of freedom for the end effector through multiple articulations of a prosthetic hand, wrist and elbow. Measuring improvement in patient function with these devices requires development of appropriate assessment tools. Objectives: This study presents a refined clothespin relocation test for measuring performance and assessing compensatory motion between able-bodied subjects and subjects with upper limb impairments. Study Design: Comparative analysis Methods: Trunk and head motions of 13 able-bodied subjects who performed the refined clothespin relocation test were compared to the motion of a transradial prosthesis user with a single degree of freedom hand. Results: There were observable differences between the prosthesis user and the able-bodied group. The assessment used provided a clear indication of the differences in motion through analysis of compensatory motion. Conclusion: The refined clothespin relocation test provides additional benefits over the standard clothespin assessment and makes identification of compensatory motions easily identifiable to the researcher. While this paper establishes the method for the new assessment, further validation will need to be performed with more users

Minority environmental activism in Britain: From brixton to the lake district

Historically, the British environmental movement has been devoid of minority participation, but this is changing very slowly, with the emergence of ethnic minority environmental groups and multiracial environmental alliances. These groups have argued that ethnic minorities have little or no access to public funds earmarked for countryside and wildlife preservation issues. They argue that white environmental organizations do not pay attention to the needs of inner-city minority residents and minority access to the countryside. Increased access, community improvement and beautification projects, environmental education, youth training, community garden projects, and issues of environmental racism are all foci of ethnic minority environmental movements. While some white environmentalists have been supportive of them, others have been uncomfortable with them or even hostile to their existence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43549/1/11133_2004_Article_BF00990102.pd

Climate, human behaviour or environment: individual-based modelling of Campylobacter seasonality and strategies to reduce disease burden

Acknowledgements: We thank colleagues within the Modelling, Evidence and Policy Research Group for useful feedback on this manuscript. Competing interests: The authors declare that they have no competing interests. Availability of data and materials: The R code used in this research is available at https://gitlab.com/rasanderson/campylobacter-microsimulation; it is platform independent, R version 3.3.0 and above. Funding: This research was funded by Medical Research Council Grant, Natural Environment Research Council, Economic and Social Research Council, Biotechnology and Biological Sciences Research Council, and the Food Standards Agency through the Environmental and Social Ecology of Human Infectious Diseases Initiative (Sources, seasonality, transmission and control: Campylobacter and human behaviour in a changing environment (ENIGMA); Grant Reference G1100799-1). PRH, SJO’B, and IRL are funded in part by the NIHR Health Protection Research Unit in Gastrointestinal Infection, at the University of Liverpool. PRH and IRL are also funded in part by the NIHR Health Protection Research Unit in Emergency Preparedness and Response, at King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.Peer reviewedPublisher PD