A Method for Removing ECG Interference From Lumbar EMG Based on Signal Segmentation and SSA

Hou, Chao; Cai, Fenglun; Liu, Fei; Cheng, Shuhong; Wang, Hongbo

doi:10.1109/jsen.2022.3179434

Cited by 5 publications

(1 citation statement)

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“…The central frequency was 25 Hz, and the system took 300 samples around it in 4 s, where the first and the last second usually contained errors due to a person’s delayed response to activate their muscles. Then, people rested for 4 s between experiments to eliminate the effect of muscle fatigue [ 41 ]. This process was performed ten times, where people intentionally made each emotion by their criteria, representing 300 instances per expression.…”

Section: Machine Learning Pipelinementioning

confidence: 99%

Portable Facial Expression System Based on EMG Sensors and Machine Learning Models

Sanipatín-Díaz,

Rosero-Montalvo,

Hernandez

2024

Sensors

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One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the dataset was made. On the other side, sensors can capture muscle reactions and respond on the spot, preserving information locally without using robust computers. Therefore, the research subject is the recognition of the six primary human emotions using electromyography sensors in a portable device. They are placed on specific facial muscles to detect happiness, anger, surprise, fear, sadness, and disgust. The experimental results showed that when working with the CortexM0 microcontroller, enough computational capabilities were achieved to store a deep learning model with a classification store of 92%. Furthermore, we demonstrate the necessity of collecting data from natural environments and how they need to be processed by a machine learning pipeline.

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Section: Machine Learning Pipelinementioning

confidence: 99%