M. Zhu (Ed.): Electrical Engineering and Control, LNEE 98, pp. 801–808. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

A Research on the Application of Physiological Status Information to Productivity Enhancement

Qingguo Ma, Qian Shang, Jun Bian, and Huijian Fu

School of management, Zhejiang University, Hangzhou, 310027

Abstract. This paper is grounded in the field of Neuro-Industrial Engineering, focuses on humans, takes human’s physiological status data (e.g. EEG, EMG, GSR and Temp) into account and applies biofeedback technology to investigate the application of physiological status in enhancing productivity, and thus im-proves production efficiency and increases the profitability of certain enterpris-es, which will be beneficial to the long-term development of the enterprises and harmonious development of the whole society.

Keywords: Neuro-Industrial Engineering (Neuro-IE), physiological status in-dexes, productivity, Neuromanagement.

1 Introduction

1.1 The Development of Industrial Engineering

Industrial Engineering is a discipline that designs, improves and implements the inte-grated system formed by people, material, equipment, energy resources, information, etc, which emphasizes on improving productivity, reducing the costs and insuring quality in order to obtain maximum overall effectiveness with the production system operating in the best condition. Thus it has been attached great importance by many countries in recent decades.

The development of Industrial Engineering has experienced three notable stages as “Scientific Management Era”, “the Comprehensive Improvement after Wars” and “Human Factors Period”, each of which has played an important role in facilitating enterprise production and social development in their own times. In the course of the development of IE, Taylor’s Theory of Scientific Management was an important thought about motion study and time research[1], which generated 17 elemental mo-tions and standardized operations procedure; in the stage of “the Comprehensive Im-provement after Wars”, Enterprise Resource Planning (ERP), Toyota Production System, Total Quality Management and other classic production and management methods have improved productivity by using digital information; And in the stage of “Human Factors Period”, with the realization of the effectiveness of human itself, engineers have combined psychology, anatomy and anthropometry with engineering design and operations management, trying their best to improve work environment[2].

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1.2 Existing Problems

Traditional researches overemphasized on productivity and standardization, taking humans as machines, even work environment and equipments are improved in the stage of “Human Factors Period”, attention has not yet been drawn to human itself, which makes human a “living machine” and causes accidents such as “13 leaps” of Foxconn employees in Shenzhen, 2010. Though there are factors related to failed management and low payment, the advent of expressions such as “sweetshop” and “industrialized apathy” has reflected workers’ true conditions in factories and largely discredited Foxconn. Meanwhile, a high degree of fatigue is induced by excess work-load, which causes tremendous potential safety hazards. It’s reported in the US that the loss is about 18 billion every year caused by work fatigue in industrial production, and at least 12 billion by transportation accidents on expressway.

Traditional production mode could help enterprises gain advantages of high quali-ty, high efficiency, low cost and quick response, but it doesn’t consider human emo-tion and cognition, neglecting people’s physiological and psychological factors by taking people merely as a part of the production line. However, with the progress of science and the improvement of living standard, workers’ job satisfaction is no more determined only by acquiring economic benefits. People’s body structure, physical function, physical and emotional states and emotional changes influence their psycho-logical and physiological status, which further influence people’s external perfor-mance. Thus it’s significant to consider “people’s psychological and physiological status” as an important parameter in production management.

2 The Introduction of Neuro-industrial Engineering

Given the deficiencies of traditional research, Neuro-Industrial Engineering engen-dered by the combination of neuroscience and industrial engineering has provided resolutions. In 2006, Prof. Ma (the supervisor of the Neuromanagement lab at Zhe-jiang University of China) et al. firstly advanced the concept of “NeuroIE” [3]. Based on people’s physiological status, NeuroIE obtains objective and actual data by mea-suring human brain and physiological indexes with advanced neuroscience tools and biofeedback technology, analyzes the data, adds neural activities, physiological status in production process as new factors into operations management, and finally realizes man-machine integration by adjusting work environment and production system ac-cording to people’s response to the system, avoiding accidents and improving effi-ciency and quality.

Biofeedback technology is used in this research. This technology amplifies physio-logical process and bioelectrical activities inside human body with the help of elec-tronic instrument. Amplified information of electrical activities is presented in visual (e.g. gauge reading) or auditory (e.g. humming) forms, which enable the subject to recognize its own physiological status and learn how to control and rectify abnormal physiological changes to a certain extent. The data collected by Biofeedback instru-ment includes myoelectricity, skin temperature, brain wave activities, skin conductivi-ty, blood pressure, heart rate, etc, all of which could reflect individual’s physiological and psychological status.

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This research is grounded on Holley Metering Limited. We are enabled to infer workers’ physiological and psychological status after collecting and analyzing the indexes listed above in the course of production, and then make appropriate adjust-ment and arrangement according to the characters and requirements of certain stations in order to radically remove hidden dangers caused by workers’ physiological status (e.g. fatigue) and bad mood (e.g. boredom).

3 Result and Conclusion

This research is based on Holley Metering Limited, and we choose two key stations (i.e. input program station and maintenance station) in the workshop after surveys and interviews, because there are significant differences between these two stations in terms of task characteristics, which mean they could be analyzed as typical stations in the production line. Meanwhile, both new and old employees, between whom there is obvious difference in job performance, are selected in these two stations respectively.

Portable biofeedback instrument is used to measure and record the selected em-ployees’ physiological indexes (e.g. EEG, EMG, GSR and Temp). Then the data are analyzed with correspondent biofeedback technology, and the following conclusions are drawn from the analysis.

3.1 The Rule of Physiological and Psychological Changes That Influence Job Performance

EEG data. EEG is mainly used to measure and record brain electrical activities (amplitude and frequency) in different brain areas, and the rhythm and amplitude of brain electrical activities are closely related to emotion and attention[4].

Regarding resource utilization of left and right brain, it occupies more right brain resource in the station of input program because it needs standardized and formalized operations and the procedures are fixed and simple, meaning it needs more spatial thinking, which is exactly the function of right brain. While in maintenance station, it occupies more resource in left brain, because the specific attribute of the station re-quires richer work experience, more thinking and judgment, which is mainly the func-tion of left brain.

Table 1. The frequency and description of EEG[4]

Brain waves Frequency Description of indexes Θ waves 4~7HZ Level of fatigue

SMR waves 12~15HZ Level of attention Β waves 13~40HZ Level of excitation

Seeing from the brain resource utilization of new and old workers, in input pro-gram station, both the variation values of θ and Β waves of new worker’s are higher than those of old worker’s, meaning the new worker has a higher level of fatigue and is emotionally nervous because of nonproficiency in operations. But it’s opposite in maintenance station as the variation values of θ and SMR waves of the old worker’s

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are higher, implicating a higher level of fatigue and attention, which is mainly the result of more occupancy of brain resource and attention caused by deeper and more comprehensive thinking.

Fig. 1. EEG data of input program station and maintenance station

EMG data. EMG measures myoelectricity voltage on body surface. There is close relation between myoelectricity and the level of muscular tension as EMG rises rapidly when muscles are tense, and vice versa. Thus EMG is a significant indicator of performance intensity and emotion[5].

In view of EMG data, in input program station, the old worker’s right myoelectrici-ty voltage is higher than left, while it’s opposite for the new worker, because for an old worker, he has to fetch objects in a greater spatial extension with right hand in the process, which raises up right myoelectricity voltage by greater movement range, but for the new worker, a specialized worker is assigned to help him fetch needed objects and the new worker has to put the parts after processing on the conveyor belt, which raises up left myoelectricity voltage. In maintenance station, the old worker’s right myoelectricity voltage is higher than left, and the new worker’s is opposite, because normally left hands are used to hold and invert the device, while as a result of inexpe-rience, the new worker has to check and test the device repeatedly so as to find out the faults, which raises up left myoelectricity voltage.

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Fig. 2. EMG data of input program station and maintenance station

Comparing EMG data of new and old workers, new workers’ myoelectricity vol-tage are higher than old workers’ in both stations, because new workers are inexpe-rienced and need repeated inspections and tests in order to find the correct operation points.

GSR and Temp data. GSR feedback reflects the changes of sympathetic nerves’ sys-tematic activities by measuring the changes of sweat glands’ activities. In the situation of emotional strain, horror and anxiety, the sweat glands excretes more sweat, which causes the skin conductance to rise and increases GSR. Thus GSR is the most remark-able indicator of emotional changes and could be used to relieve emotional strain. Temp is also an indicator of emotional change that is in accordance with GSR, as when a person is relieved, the excitation of sympathetic nerves declines and blood flow volume at finger tip increases, but when a person is nervous, the excitation of sympathetic nerves escalates and blood flow volume at finger tip decreases, which causes the skin temperature to fall.

In input program station, old worker’s GSR value firstly declines and then climbs up, suggesting anxiety induced by fatigue after a period of work, and is notably higher than the new worker on the whole. And seen from the following figure, new and old workers’ Temp value changes are small and there is no significant difference between them.

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Fig. 3. GSR and Temp data of input program station and maintenance station

In maintenance station, new and old workers’ GSR value changes are small and there is no significant difference between them, too. Regarding Temp, old workers’ Temp value change is relatively small, while the temperature of the new worker firstly falls dramatically and then recovers gradually, suggesting emotional strain and anxiety at the beginning of maintenance work, and emotional status recovers after entering a stable working state.

3.2 The Best Rest Point to Minimize the Probability of Errors and Realize the Balance between Quality and Efficiency

The design of rest system is generally based on the level of physical and mental fati-gue. Physiological and psychological indicators enable us to determine the best rest time point and adjust rest system from a new perspective. Among those brain waves, Theta waves rise with the escalation of fatigue level, and SMR waves with the escala-tion of attention and vigilance. When a person starts to feel fatigue, the probability of errors increases and it requires more attention and vigilance to keep on working. Based on that rule, a best rest point could be obtained to minimize the probability of errors.

As is shown in the figure, compared with input program station, maintenance sta-tion requires more elaborate operations and brain resource. Seen from the variation tendency, in maintenance station, EEG wavebands firstly rise, then fall, and after that climb up again before reaching the peak; but in the station of input program station, EEG wavebands rise placidly, meaning fatigue level rises slowly, and then reach the peak. In traditional management patterns, time point for rest is usually set at the latter fatigue point, neglecting the increase of the probability of errors caused by no rest at

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the first fatigue point for workers at stations that require more brain resource (e.g. maintenance station). Thus this research facilitates the design of rest system for the management.

Fig. 4. The variations of Theta vaves and SMR waves of input program station and mainten-ance station

4 Prospect

The advent of Neuro-IE has pushed forward the development of industrial engineer-ing to a new level, and the application of biofeedback technology in enterprises, which for the first time brings physiological and psychological status of “human it-self” as significant factors into the research of production efficiency and provides a new perspective to investigate the way to enhance employees’ work efficiency. It plays a major role in the deepening of industrial engineering theory and is of great realistic significance and application value for modern production and social devel-opment.

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