EMG-based analysis of change in muscle activity during simulated driving

Balasubramanian, Venkatesh; Adalarasu, K.

doi:10.1016/j.jbmt.2006.12.005

Cited by 85 publications

(42 citation statements)

References 36 publications

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“…All of the signal data were sampled at a frequency of 1 kHz, and filtered with a pass band range of 15-500 Hz (sEMG data) [43], 30 Hz (ECG data), and 10 Hz (breathing data) by the QP-700H ® software (Nihon Kohden, Tokyo, Japan). ECG data were analyzed using a self-developed program, Enthought Canopy 1.5.5 (Enthought, Inc., Austin, TX, USA) to determine the R-R interval.…”

Section: Resultsmentioning

confidence: 99%

Community Gardens as Health Promoters: Effects on Mental and Physical Stress Levels in Adults with and without Mental Disabilities

et al. 2017

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Abstract:The study focuses on psychological and physical effects of stress while performing community garden activities of various intensity levels. The aim of this study was to determine the psychological and physical effects in adults with (case group) and without (control group) mental disabilities. Salivary α-amylase (sAA) levels and the stress response scale (SRS-18) were used for the psychological analysis (n = 42). For physical assessment (n = 13), electrocardiogram (ECG), surface electromyogram (sEMG), and respiration rate were continuously measured while performing the activities using a multichannel telemetry system. The results showed that following the activities, the case group exhibited decreasing sAA levels while control group exhibited increasing sAA levels. However, both groups exhibited lower SRS-18 results following the activities. Compared with the control group, the case group had a significantly lower increase in the ratio of the heart rate (IRHR) (5.5%) during low-intensity work (filling pots with soil), but a significantly higher IRHR (16.7%) during high-intensity work (turning over soil). The case group experienced significantly higher levels of fatigue during high-intensity work (digging) than during the rest condition. These findings indicate that appropriate workload allocation, according to health, is necessary in the community garden setting because reducing the intensity of work assignments for people with mental disabilities will reduce their physical stress.

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Section: Resultsmentioning

confidence: 99%

Community Gardens as Health Promoters: Effects on Mental and Physical Stress Levels in Adults with and without Mental Disabilities

et al. 2017

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“…sEMG signals were recorded at a sampling frequency of 1000 Hz using EMG machine (Bagnoli-8TM, Delsys Inc, USA) with single differential electrode configuration and gain of 1000. EMG machine was powered by an isolated medical grade power supply with leakage current less than 10 mA and safely isolated to 3750 Vrms (Balasubramanian and Adalarasu, 2007).…”

Section: Semg Acquisitionmentioning

confidence: 99%

“…Muscle fatigue can be measured indirectly using the knowledge of direction and magnitude of the slope of line of best fit (Balasubramanian and Adalarasu, 2007). Hence in this study linear regression analysis was performed on mean amplitude, rms from time domain and MF values extracted from frequency domain of sEMG signals obtained from the muscles (trapezius and erector spinae) of each subject during pre-and post-flight condition.…”

Section: Regressionmentioning

confidence: 99%

Analysis of muscle fatigue in helicopter pilots

2011

Self Cite

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“…SEMG is a comprehensive effect of superficial muscle electrical signals and electrical activity on the skin surface, which can reflect the neuromuscular activity to some extent [15]. When the driver's fatigue degree deepens, the frequency of EMG signal will continue to decline, the amplitude gradually increased [16]. Therefore, the analysis of EMG signals can achieve the purpose of the driver's fatigue level.…”

Section: Emg Detectionmentioning

confidence: 99%

A Review on Fatigue Driving Detection

Shi

Wang

2017

ITM Web Conf.

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Abstract:The socialization of automobile development has brought great convenience to people's travel. However, the rapid increase in the number of vehicles has also caused a series of problems. The increase in traffic accidents has brought great social casualties and economic losses. Fatigue driving, which is an important factor in the traffic accident, has aroused people's attention. This paper reviews all kinds of fatigue driving detection methods at present; compares various fatigue driving detection methods in terms of accuracy, real-time and cost; analyses the advantages and disadvantages of various methods; introduces the application of fatigue detection system in automobile; summarizes the current deficiencies and future development trends in the field of fatigue driving detection. The future research of this field will be more to the data fusion, computer vision and deep learning.

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EMG-based analysis of change in muscle activity during simulated driving

Cited by 85 publications

References 36 publications

Community Gardens as Health Promoters: Effects on Mental and Physical Stress Levels in Adults with and without Mental Disabilities

Community Gardens as Health Promoters: Effects on Mental and Physical Stress Levels in Adults with and without Mental Disabilities

Analysis of muscle fatigue in helicopter pilots

A Review on Fatigue Driving Detection

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