Overcoming the effect of muscle fatigue on gesture recognition based on sEMG via generative adversarial networks

Prediction and classification of sEMG-based pinch force between different fingers

Difference analysis of musculation and estimation of sEMG-to-force in process of increasing force and decreasing force

Adaptive detection of Ahead-sEMG based on short-time energy of local-detail difference and recognition in advance of upper-limb movements

Spatial averaging method based on adaptive weight for imaging photoplethysmography

A real-time heart rate estimation framework based on a facial video while wearing a mask

BACKGROUND: The imaging photoplethysmography (iPPG) method is a non-invasive, non-contact measurement method that uses a camera to detect physiological indicators. On the other hand, wearing a mask has become essential today when COVID-19 is rampant, which has become a new challenge for heart rate (HR) estimation from facial videos recorded by a camera. OBJECTIVE: The aim is to propose an iPPG-based method that can accurately estimate HR with or without a mask. METHODS: First, the facial regions of interest (ROI) were divided into two sub-ROIs, and the original signal was obtained through spatial averaging with different weights according to the result of judging whether wearing a mask or not, and the CDF, which emphasizes the main component signal, was combined with the improved POS suitable for real-time HR estimation to obtain the noise-removed BVP signal. RESULTS: For self-collected data while wearing a mask, MAE, RMSE, and ACC were 1.09 bpm, 1.44 bpm, and 99.08%, respectively. CONCLUSION: Experimental results show that the proposed framework can estimate HR stably in real-time in both cases of wearing a mask or not. This study expands the application range of HR estimation based on facial videos and has very practical value in real-time HR estimation in daily life.

Target Detection in Infrared Image of Transmission Line Based on Faster-RCNN

Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG

Research on Non-contact Multi-person Heart Rate Measurement Method for Intelligent Education

A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method

Notice of Removal: A New Framework for Robust Heart Rate Measurement Based on the Head Motion State Estimation

PS-GAN: a single image snow removal framework using pseudo-Siamese GANs

Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging

Research on Image Recognition Technology of Transmission Line Icing Thickness Based on LSD Algorithm

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

Encrypted digital watermarking algorithm for quick response code using discrete cosine transform and singular value decomposition

Identification of Gesture Based on Combination of Raw sEMG and sEMG Envelope Using Supervised Learning and Univariate Feature Selection

Research on Recognition of Nine Kinds of Fine Gestures Based on Adaptive AdaBoost Algorithm and Multi-Feature Combination

Gesture recognition method based on a single-channel sEMG envelope signal

Periodic characteristics of the Josephus ring and its application in image scrambling