A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning

Tam, Simon; Boukadoum, Mounir; Campeau-Lecours, Alexandre; Gosselin, Benoît

doi:10.1109/tbcas.2019.2955641

Cited by 110 publications

(65 citation statements)

References 22 publications

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“…Recently, the application of machine learning in the field of ergonomics is increasing. Several studies predicted human hand gestures by analyzing EMG data [35,36] or estimated a three-dimensional posture based on images using a CNN [37,38]. While deep learning studies predicting gestures and three-dimensional postures have been actively conducted, relatively few studies have investigated to predict grip strength.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

Hwang

Lee

2021

PLoS ONE

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The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.

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Section: Introductionmentioning

confidence: 99%

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

Hwang

Lee

2021

PLoS ONE

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“…The other researches shows the same conclusion [15]. The authors of [16] make a single high-density surface electromyography (HD-sEMG) dry electrodes device by a matrix of sensor nodes. A 3-layer CNN with a majority vote on 5 successive inferences is used to recognize 8 hand postures and the accuracy reaches 98.15%.…”

Section: Related Workmentioning

confidence: 60%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

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“…Other studies have drawn the same conclusions [15]. In [16], a single high-density surface electromyography (HD-sEMG) dry electrode device was constructed using a matrix of sensor nodes. A triple-layer CNN with a majority vote on five successive inferences was used to recognize eight hand postures, achieving an accuracy of 98.15%.…”

Section: Introductionmentioning

confidence: 71%

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Zhao

Bao

et al. 2021

Sensors

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When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

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A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning

Cited by 110 publications

References 22 publications

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

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