EMG Characterization and Processing in Production Engineering

Olmo, Manuel del; Domingo, Rosario

doi:10.3390/ma13245815

Cited by 16 publications

(17 citation statements)

References 113 publications

(154 reference statements)

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“…A linear filter was applied to the signal once forward and once backward to avoid a frequency-dependent phase shift. The EMG signals were filtered using a fourth-order bandpass (20 Hz-450 Hz) Butterworth filter, which is one of the most commonly used solutions for filtering EMG signals [63], [64]. To normalize the signals, they were divided by their maximum value [64].…”

Section: ) Signal Processingmentioning

confidence: 99%

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

et al. 2022

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Virtual Reality (VR) video games have become popular in recent years with the emergence of a wide variety of enhanced VR hardware systems. Typically, current VR video games incorporate high-quality auditory and video feedback along with vibro-tactile cues to provide an immersive experience. However, the ongoing VR hardware and software do not consider the state of the user to determine whether the video game is generating an enjoyable experience, or if it has become a stressing or boring experience. This work provides an assessment of stress level estimation from features extracted from Electrocardiography (ECG), Electrodermal Activity (EDA), and Electromyography (EMG) signals of users while playing a VR video game with different difficulty levels. Statistical differences were found between the rest and gaming stages for several extracted features. Regardless of the fact that no significant statistical differences between the three levels of difficulty were found by analyzing the EDA and ECG features, an 83.1% accuracy was obtained for the classification between the three levels with a KNN model. For the EMG signal, the obtained accuracy ranged between 99% and 100% for the distinction between a difficult level 1 and rest stage for all models. When all features from the ECG, EDA and EMG signals were used, an accuracy of 99% was obtained for the differentiation between the three difficulty levels and a resting stage. The presented work results may serve as a reference for future work regarding feature extraction and game difficulty adaptation for user experience enhancement.

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Section: ) Signal Processingmentioning

confidence: 99%

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

et al. 2022

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“…Further information in this area can be obtained from. 45,46 The research on composite layup in Prabhu et al 7,42 is particularly relevant because most manual processes in electrical machine manufacture for example, winding, wiring, making end connections, etc. involve handling deformable material similar to composite hand layup process.…”

Section: State Of the Artmentioning

confidence: 99%

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

Tiwari

Farnsworth

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, several manufacturing operations continue to involve manual intervention because of the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances of the final machine, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in in-process monitoring particularly for error prone manual operations. This paper presents a literature review of the state-of-the-art available techniques and limitations in process monitoring within the context of electrical machine manufacturing. To quantify the degree of manual activities in process monitoring within electrical machine manufacture, a structured survey of UK based companies was conducted, identifying specific error prone manual processes to target, and gaps in inspection. The survey identified that a significant proportion of activities in electrical machine manufacture are manual, or semi-automated with manual interventions. However, literature review revealed only a limited research in in-process monitoring of manual operations in this area. Finally, two case studies are presented where case study 1 presents a framework for digitisation of a variety of manual manufacturing tasks, and case study 2 demonstrates real-time capture, modelling and analysis of deformable linear objects in electrical machine manufacturing.

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“…An electromyogram (EMG) is a biomedical and clinical signal, that is, electrical currents generated by neuromuscular activities during muscle contraction. − In particular, surface EMG (sEMG) electrodes collect muscular currents from the surface of the skin, and they have been particularly attractive for wearable systems for various human activities and health care monitoring. − …”

Section: Introductionmentioning

confidence: 99%

A Coiled Carbon Nanotube Yarn-Integrated Surface Electromyography System To Monitor Isotonic and Isometric Movements

Jang

Moon

Lee

et al. 2022

ACS Appl. Mater. Interfaces

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A surface electromyogram (sEMG) electrode collects electrical currents generated by neuromuscular activity by a noninvasive technique on the skin. It is particularly attractive for wearable systems for various human activities and health care monitoring. However, it remains challenging to discriminate EMG signals from isotonic (concentric/eccentric) and isometric movements. By applying nanotechnology, we provide a coiled carbon nanotube (CNT) yarn-integrated sEMG device to overcome sEMG-based motion recognition. When the arm was contracted at different angles, the sEMG-derived root mean square amplitude signals were constant regardless of the angle of the moving arm. However, the coiled CNT yarn-derived open circuit voltage (OCV) signals proportionally increased when the arm’s angle increased, and presented negative and positive values depending on the moving direction of the arm. Moreover, isometric contraction is characterized by the onset of EMG signals without an OCV signal, and isotonic contraction is determined by both EMG signals and OCV signals. Taken together, the integration of EMG and coiled CNT yarn electrodes provides complementary information, including the strength, direction, and degree of muscle movement. Therefore, we suggest that our system has high potential as a wearable system to monitor human motions in industrial and human system applications.

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EMG Characterization and Processing in Production Engineering

Cited by 16 publications

References 113 publications

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

Stress Level Estimation Based on Physiological Signals for Virtual Reality Applications

In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions

A Coiled Carbon Nanotube Yarn-Integrated Surface Electromyography System To Monitor Isotonic and Isometric Movements

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