Muscle fatigue assessment in manual handling of loads using motion analysis and accelerometers: A short review

Bernardo, Fábio; Martins, Raquel; Guedes, Joana

doi:10.1201/9781351008884-12

Cited by 6 publications

(2 citation statements)

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“…The proposed method of reading signals in [9] requires a driver to wear cloth of thickness less than 2 mm. Besides, there are also methods to use sEMG sensors to analyze muscle signals and detect fatigue driving [31]. However, they require sEMG sensors to be directly attached on the human hands, skin or neck, which may not be practical [29].…”

Section: Physiological Based Methodsmentioning

confidence: 99%

Can Steering Wheel Detect Your Driving Fatigue?

Xi²,

Zhang³

et al. 2020

Preprint

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Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the driver's anomalous behaviour is closely monitored by the system. Though many approaches have been proposed for detecting driver fatigue, they largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores about 90%. We also propose promising future directions to deploy this approach in reallife settings, such as applying multimodal learning using several supplementary sensors.

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Section: Physiological Based Methodsmentioning

confidence: 99%

Can Steering Wheel Detect Your Driving Fatigue?

Xi²,

Zhang³

et al. 2020

Preprint

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“…Optimal movement variations are recognized as factors delaying the development of fatigue during a prolonged activity (Bartlett et al, 2007; Sedighi & Nussbaum, 2017; Srinivasan & Mathiassen, 2012) by distributing load across active-passive tissues, thus maintaining performance (Bauer et al, 2017; van Dieën et al, 2003). Depending on the region and extent of fatigue, various behavioral changes have been observed including lifting style, switching between stoop and squat postures (Bonato et al, 2003), and accelerated forward bending (Bernardo et al, 2018; Chen, 2000).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Spine Kinematics, Kinetics, and Trunk Muscle Activities During Fatigue-Induced Repetitive Lifting

et al. 2021

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Objective Spine kinematics, kinetics, and trunk muscle activities were evaluated during different stages of a fatigue-induced symmetric lifting task over time. Background Due to neuromuscular adaptations, postural behaviors of workers during lifting tasks are affected by fatigue. Comprehensive aspects of these adaptations remain to be investigated. Method Eighteen volunteers repeatedly lifted a box until perceived exhaustion. Body center of mass (CoM), trunk and box kinematics, and feet center of pressure (CoP) were estimated by a motion capture system and force-plate. Electromyographic (EMG) signals of trunk/abdominal muscles were assessed using linear and nonlinear approaches. The L5-S1 compressive force (Fc) was predicted via a biomechanical model. A two-way multivariate analysis of variance (MANOVA) was performed to examine the effects of five blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as independent variables, on kinematic, kinetic, and EMG-related measures. Results Significant effects of lifting trial blocks were found for CoM and CoP shift in the anterior–posterior direction (respectively p < .001 and p = .014), trunk angle ( p = .004), vertical box displacement ( p < .001), and Fc ( p = .005). EMG parameters indicated muscular fatigue with the extent of changes being muscle-specific. Conclusion Results emphasized variations in most kinematics/kinetics, and EMG-based indices, which further provided insight into the lifting behavior adaptations under dynamic fatiguing conditions. Application Movement and muscle-related variables, to a large extent, determine the magnitude of spinal loading, which is associated with low back pain.

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