Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Li, Wenguo; Luo, Zhizeng; Jin, Yingji; Xi, Xu

doi:10.3390/s21010119

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…By collecting the sEMG signals of specific muscle groups and analyzing the pattern information, nine corresponding gesture can be recognized from the sEMG signals of four muscle groups. The specific steps can be found in [19]. The definitions of the nine gestures and the corresponding CSL vocabulary are shown in (Fig 8).…”

Section: Gesture Recognitionmentioning

confidence: 99%

Chinese sign language recognition based on surface electromyography and motion information

Li,

Luo,

et al. 2023

PLoS ONE

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Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR). Based on the inherent correlation between gesture and trajectory of SL action, SLR is organically divided into gesture-based recognition and gesture-related movement trajectory recognition. One hundred and twenty commonly used Chinese SL words involving 9 gestures and 8 movement trajectories, are selected as research and test objects. The method based on the amplitude state of surface electromyography (sEMG) signal and acceleration signal is used for vocabulary segmentation. The multi-sensor decision fusion method of coupled hidden Markov model is used to complete the recognition of SL vocabulary, and the average recognition rate is 90.41%. Experiments show that the method of sEMG signal and motion information fusion has good practicability in SLR.

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Section: Gesture Recognitionmentioning

confidence: 99%

Chinese sign language recognition based on surface electromyography and motion information

Li,

Luo,

et al. 2023

PLoS ONE

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“…Moreover, objects add noise to the gesture detection pipeline and increase the difficulty of hand segmentation. Previous research explored several methods for on-object gesture detection, such as infrared proximity sensors allowing, for example, multi-touch interaction around small devices [ 11 ]; capacitive sensing techniques enabling the detection of touch events on humans, screens, liquids, and everyday objects [ 12 ]; electromyography systems that measure muscle tension [ 13 , 14 , 15 ]; and even acoustic sensing systems [ 16 , 17 , 18 , 19 ]. The latter range from commercial miniature ultrasonic sensors on chips to recent developments in ultrasonic gesture sensing methods through on-body acoustics.…”

Section: Related Workmentioning

confidence: 99%

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Attygalle

Leiva

Kljun

et al. 2021

Sensors

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Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

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“…This data can enhance the characterization of neuromuscular impairments, while tracking the changes in muscle activity from baseline when neurorehabilitation interventions are administered. Clinically, sEMG is frequently used to obtain a precise and objective measure of muscle activity during motor performance [21][22][23][24][25]. EMG is useful to assess hyperactivity and inactivity of selected muscles [26] and, given that it can be used to evaluate the integrity of neuromuscular system, it is often adopted as a physiological biofeedback in physical therapy [27].…”

Section: Introductionmentioning

confidence: 99%

Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?

Kiper

Rimini

Falla

et al. 2021

Sensors

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It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.

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Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Cited by 8 publications

References 21 publications

Chinese sign language recognition based on surface electromyography and motion information

Chinese sign language recognition based on surface electromyography and motion information

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?

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