Whole body fatigue detection is an important phenomenon and the factors contributing to whole body fatigue can be controlled if a mathematical model is available for its assessment. This research study aims at developing a model that categorizes whole body exertion into fatigued and non-fatigued states based on physiological and perceived variables. For this purpose, logistic regression was used to categorize the fatigued and non-fatigued subject as dichotomous variable. Normalized mean power frequency of eight muscles from 25 subjects was taken as physiological variable along with the heart rate while Borg scale ratings were taken as perceived variables. The logit function was used to develop the logistic regression model. The coefficients of all the variables were found and significance level was checked. The detection accuracy of the model for fatigued and non-fatigues subjects was 83% and 95% respectively. It was observed that the mean power frequency of anterior deltoid and the Borg scale ratings of upper and lower extremities were significant in predicting the whole body fatigued when evaluated dichotomously (p < 0.05). The findings can help in better understanding of the importance of combined physiological and perceived exertion in designing the rest breaks for workers involved in squat lifting tasks in industrial as well as health sectors.

본 연구는 피부전도도(skin conductance response), 말초혈관 반응(peripheral plethysmograph), 피부온도 (skin temperature)와 심박수(heart rate)와 같은 다양한 생리신호를 측정하여 운전자의 심리반응을 통하여 적정 운전상태를 분석하고자 하였다. 운전자의 심리반응을 측정 및 분석하기 위하여 하드웨어와 알고리듬을 개발하였으며, 운전상황은 자동차 시뮬레이터를 이용하여 다양한 가상 운전상황을 설정하고 생리신호를 측정하였다. 측정 결과 운전자 생리신호가 운전환경의 변화에 따른 심리적 긴장 또는 이완 수준을 잘 반영하고 있고, 감정적 변화에는 다소 둔감한 것으로 나타났다. 이를 심리적 순위척도를 이용하여 정량화하여 심리적 적정 운전범위를 제안하였다. 본 연구의 결과는 추후 운전자의 안전운전이나 건강상태를 모니터링 하기 위한 시스템 개발의 자료로 활용할 수 있을 것이다.