Detection and estimation of mental fatigue in manual assembly process of complex products

Abstract

Purpose This paper aims to investigate an approach for mental fatigue detection and estimation of assembly operators in the manual assembly process of complex products, with the purpose of founding the basis for adaptive transfer and demonstration of assembly process information (API), and eventually making the manual assembly process smarter and more human-friendly. Design/methodology/approach The proposed approach detects and estimates the mental state of assembly operators by electroencephalography (EEG) signal recording and analysis in an engine assembly experiment. When the subjects perform assembly tasks, their EEG signal is recorded by a portable EEG recording system called Emotiv EPOC+ headset. The feature set of the EEG signal is then extracted by calculating its power spectrum density (PSD), followed by data dimension reduction based on principal component analysis (PCA). The dimension-reduced data are classified by using support vector machines (SVMs), and hence, the mental state of assembly operators can be estimated during the assembly process. Findings The experimental result shows that the proposed approach is able to estimate the mental state of assembly operators within an acceptable accuracy range, and the PCA-based dimension reduction method performs very well by representing the high-dimensional EEG feature set with just a few principal components. Originality/value This paper provides theoretical and experimental basis for the API transfer and demonstration based on human cognition. It provides a new idea to seek balance between the improvement of production efficiency and the sustainable utilization of human resources.

Neuroergonomics on the go. A preview of the potential of mobile EEG for work-place evaluation and design

“…As expected, (mental) fatigue and drowsiness constituted a high fraction of these studies, in particular for different aspects of aircraft piloting (Guo, Pan, Zhao, Cao, & Zhang, 2018;Hankins & Wilson, 1998;Lin et al, 2014;Rohit et al, 2017;Wilson, 2002;X. L. Zhang et al, 2017;Zhou et al, 2018), manual assembly processes (Xiao, Duan, Zhang, & Li, 2018), or in the context of a logistics workplace (Wascher, Heppner, et al, 2014a;Wascher et al, 2016). These studies included user state examinations, the development of countermeasures to critical aspects of safety in the workplace (Zhou et al, 2018), or a brain-computer interface (BCI) based control of driving speed (Z. T. Zhang et al, 2016).…”

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

“…We used a wearable smart T-shirt to record the researchers’ ECG signals, and this device can easily record the signals without any problem. Previously, many researchers have used different wearable smart devices to record the physiological signals to detect the mental stress of the assembly-line operators, brain-injured patients, drivers, equipment operators, pilots, security guards, and traffic controllers [ 28 , 31 , 102 , 103 , 104 , 105 ]. They employed various techniques to find a more accurate detection system using wearable smart devices such as SYMTOP NT9200, NI USB-6008, ST-BTA, Storm 3G Ranger X, ADS1292R, Emotiv EPOC, and HD-BTA ( Table 5 ).…”

“…Laurent et al [ 30 ] studied that multimodal information improved the detection of mental stress using physiological signals such as EEG, ECG, and EOG. Xiao et al [ 31 ] suggested that using brain signals, Principal Component Analysis (PCA) and SVM classifiers easily detect mental stress in manufacturing industry workers.…”

Revisión de electroencefalografía portable y su aplicabilidad en neurociencias.

“…Emotiv y Neurosky son quizás una de las empresas con más desarrollos, no solo para aplicaciones con videojuegos, sino en investigaciones médicas como la estimación del estado de la fatiga mental [76], déficits de la memoria de trabajo en niños con trastorno del espectro alcohólico fetal [49], regulación emocional [77], imaginación motora [25], en el movimiento de una órtesis para pacientes con accidente cerebrovascular [78], detectar somnolencia o el estado de atención en conductores [79], reconocimiento de afecto o emociones [29], evaluar procesos cognitivos en personas que juegan videojuegos [80], etc.…”