Development of EMG measurement system to control mobile robot using frontalis and zygomaticus major muscles

Mohamad, Ahmad Joraimee; Sidek, Shahrul Na’im; Mohideen, Ahmad Jazlan Haja; Ibrahim, Mohd Tarmizi; Ali, Kharudin

doi:10.1109/iecbes.2012.6498126

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The conducted analysis of decoding the relationship among facial muscles and brain is one step forward compared to the studies 1‐10 that only investigated the variations of facial muscles without considering the brain activity.…”

Section: Discussionmentioning

confidence: 99%

“…For this purpose, scientists evaluate the reaction of facial muscles through electromyography (EMG) signals analysis in different conditions. The reported investigations that decoded mental simulation and moral evaluation by analysis of facial EMG signals, 1 found out the relationship between facial EMG response and the ability of emotion perception, 2 analyzed EMG signals to find out the difference in facial muscle reaction in case of different muscles (right and left), age and sex, 3 investigated the changes in facial EMG signals in human face to face interactions, 4 controlled mobile robot by analysis of frontalis and zygomaticus major muscles, 5 analyzed the relation between EMG signals and quantitative ultrasonography of facial muscles in case of patients with facial palsy, 6 used EMG biofeedback in order to treat periorbital facial muscle tension, 7 analyzed facial muscles reaction to odors in solitude and with an audience, 8 evaluated facial EMG signals in order to recognize hedonic sensation, 9 and investigated facial expression to emotions by EMG signals analysis in patients with schizophrenia, 10 can to be mentioned.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of brain‐facial muscle connection in the static fractal visual stimulation

Soundirarajan

Babini

Sim

et al. 2020

Int J Imaging Syst Tech

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Analysis of the coupling among the brain and various organs activities is an exciting and new research area of biomedical signal analysis. We decode the correlation among the brain and facial muscle activities during rest and visual stimulation. Fractal analysis is utilized to evaluate the alterations of complexity. In this study, we present different images with different complexities to participants and evaluate how complex structures of electromyography (EMG) signals and electroencephalography (EEG) signals are related. The findings indicate that the alteration of EMG signals' complexity was significant (P value = .0001). Besides, more significant alterations in the EMG and EEG signals' complexities in response to the stimuli with higher complexities were observed. We conclude that the brain and facial muscles activities are related. We can analyze other physiological signals using this method to investigate their relationship with the brain.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of brain‐facial muscle connection in the static fractal visual stimulation

Soundirarajan

Babini

Sim

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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Design and Implementation of an Automated Robotic Bartender Using VDI 2206 Methodology

Kevin Vega-Rojas,

Samuel Andrade-Miranda,

Justiniano-Medina

et al. 2023

E3S Web of Conf.

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Today, robotics has made remarkable advances in a number of industries, including the food and beverage sector. One area of particular interest is the development of an automated bartender capable of efficiently preparing cocktails and drinks. In this context, a state-of-the-art robotic bartender has been designed and implemented following the VDI 2206 methodology, developed by the Association of German Engineers (VDI). This methodology has allowed a more precise definition of the product and accurate estimations in all phases of the mechanical, electrical, and electronic design, and for the mechanical design of the robotic bartender, it prioritizes a solid and safe structure to support the weight of the bottles and components while focusing on an attractive design to improve the user experience. The system features precise sensors to detect and measure ingredients and intelligent control algorithms to ensure accurate dosing. The user interface is intuitive, allowing for uncomplicated cocktail selection and drink customization, and Proteus software was used for the electrical and electronic design, which facilitated extensive simulation and debugging. The system incorporates a PIC16F877A microcontroller to manage and coordinate various functions such as sensors, actuators, and communications. Finally, the pilot implementation has demonstrated the system's effectiveness in preparing a wide variety of cocktails with reduced preparation time. The robotic bartender can produce 10–12 cocktails every 10 minutes, which improves the productivity of the establishments and benefits both bartenders and customers.

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Development of EMG measurement system to control mobile robot using frontalis and zygomaticus major muscles

Cited by 2 publications

References 4 publications

Analysis of brain‐facial muscle connection in the static fractal visual stimulation

Analysis of brain‐facial muscle connection in the static fractal visual stimulation

Design and Implementation of an Automated Robotic Bartender Using VDI 2206 Methodology

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