Study on personnel mental load assessment based on coal mine hoist operation

SHEN Min; LI Jizu; ZHANG Qian

doi:10.13347/j.cnki.mkaq.20241650

Abstract：In order to accurately assess the psychological load level of personnel in the process of coal mine hoist operation and ef-fectively reduce coal mine safety accidents triggered by excessive psychological load of personnel,firstly,by simulating coal mine hoist operation and combining with the N-back auditory subtask,three difficult task scenarios of low,medium and high difficulty were designed,and physiological signals(ECG,EDA and RESP)were collected from 18 experimental personnel,and at the same time,the NASA task coad index(NASA-TLX)subjective scores and the number of sub-task errors,and the normality test and hypo-thesis test were used to obtain the significance indicators.Secondly,based on the significance indexes,three machine learning meth-ods,namely,support vector machine,random forest and K nearest neighbor,were used to construct a mental load assessment model for operators.Finally,the classification accuracy of different physiological signals and combinations under the three machine learn-ing algorithms is compared.The results showed that:with the increase of sub-task difficulty,the subjective scores increased signific-antly,and the number of sub-task errors increased significantly;there were significant differences in the mean heart rate value(meanHR),the standard deviation of the heart interval(SDNN)and the ratio of the NN50 divided by the total number of NN inter-vals(PNN50)among the ECG indexes,and skin conductivity(SC)among the dermatoelectric indexes,and the respiratory indexes did not have a significant differences.The classification and recognition effects showed that:the accuracy of the constructed mental load assessment models differed in different physiological modalities;the accuracy of the classification model based on bimodal physiological signals was generally higher than that of the unimodal physiological signal classification model;and the random forest classification model in the bimodal modality(ECG+RESP)had the highest accuracy,with a recognition accuracy rate of 86.5%.