Morphological Differences in the Upper Trapezius Muscle Between Female Office Workers With and Without Trapezius Myalgia

2019 ◽  
Vol 98 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Kayleigh De Meulemeester ◽  
Patrick Calders ◽  
Jo Van Dorpe ◽  
Robby De Pauw ◽  
Mirko Petrovic ◽  
...  
Keyword(s):  
Trapezius Muscle ◽  
Office Workers ◽  
Upper Trapezius ◽  
Trapezius Myalgia ◽  
Upper Trapezius Muscle ◽  
Morphological Differences

Upper trapezius muscle activity in healthy office workers: reliability and sensitivity of occupational exposure measures to differences in sex and hand dominance

Ergonomics ◽  
2016 ◽  
Vol 59 (9) ◽  
pp. 1205-1214 ◽  
Author(s):  
Ryan J. Marker ◽  
Jaclyn E. Balter ◽  
Micaela L. Nofsinger ◽  
Dan Anton ◽  
Nathan B. Fethke ◽  
...  
Keyword(s):  
Occupational Exposure ◽  
Muscle Activity ◽  
Trapezius Muscle ◽  
Office Workers ◽  
Hand Dominance ◽  
Upper Trapezius ◽  
Upper Trapezius Muscle

scholarly journals Light Sleep Detection based on Surface Electromyography Signals for Nap Monitoring

2022 ◽  
Vol 16 ◽  
pp. 140-145
Author(s):  
Wachiraporn Aiamklin ◽  
Yutana Jewajinda ◽  
Yunyong Punsawad
Keyword(s):  
Surface Electromyography ◽  
Sleep Stage ◽  
Low Cost ◽  
Trapezius Muscle ◽  
Sleep Stages ◽  
Upper Trapezius ◽  
Emg Signal ◽  
Power Frequency ◽  
Upper Trapezius Muscle ◽  
Waking Up

This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 84% accuracy. We implement a MyoWare muscle sensor into the proposed nap monitoring device. The results showed that the proposed system is feasible for detecting sleep stages and waking up the napper. A combination of EMG and electroencephalogram (EEG) signals might be yield a high system performance for nap monitoring and alarm system. We will prototype a portable device to connect the application to a smartphone and test with a target group, such as truck drivers and physicians.

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