心智負荷(Mental Workload)是影響身心疲勞的主要因素。如果心智負荷 過重,容易導致工作人員疲勞或感到壓力大,而這些負荷可來自於職場的 壓力及各種因素,又或者是自我的期許夠高,而導致這些負荷超過自身能 力所及的範圍,這將影響到自身的工作績效,並容易在工作上產生人為因 素的問題也將隨之增加,這也將會影響到自身的情緒與健康,倘若繼續忽 視的話可能會產生暈眩、血壓升高、無力感等,甚至會演變成慢性疲勞疾 病。因此若能即時掌控員工的心智負荷,並維持適當的負荷,將能有效提 升人員的生產力及健康狀態。 在本論文中,使用繼承式基因演算法(Inheritable Bi-objective Genetic Algorithm, IBCGA)結合支援向量機制(Support Vector Machine, SVM)來預測 員工心智負荷的狀況及分析其影響因子的重要性,以確保人員在工作上能 夠保持在良好的狀態。IBCGA是由智慧型基因演算法(Intelligent Genetic Algorithm, IGA)並加入繼承式機制做最佳適應函數的求解方法,使每個候選 解(編碼在染色體內)經過SVM的評估預測正確率達到最佳化的求解結果,更 快得到最佳近似解。本文使用前人文獻中採取問卷的方式收集70組數據。 透過分析輸入數據健康(Health)、安全(Safety)、環境(Environment)、人體工 學(Ergonomic)來預測員工們的心智負荷(健康和不健康二類)。將文獻70組數 據分成三次不同的比例進行分析,以求出最佳近似解。三種方式的結果顯 示,環境與人體工學是重要的前兩項參數,尤其環境因子是決定心智負荷 中最重要的影響因子,其獨立測試的預測正確率可達73.3%。
Mental workload is the main factor affecting the physical and mental fatigue. If mental overload, easily lead to fatigue or staff feel pressure, but these loads can come from workplace stress and a variety of factors, or is it self-expectations high enough, which led to the load exceeds the capacity of the reach of their range, which will affect their job performance, and prone to human factors issues at work will also increase, which will affect their mood and health, if continue to ignore the words may produce dizziness, high blood pressure, powerlessness, and even turn into chronic fatigue illness. So if immediate control of the mental workload of staff and maintain proper load, will be able to effectively enhance the productivity and health of workers. In this paper, Use IBCGA combined with SVM to predict Staff analysis of mental workload conditions and the importance of its impact factor. IBCGA by IGA And add style inheritance mechanism to make the best method for solving the fitness function, Candidate solutions after assessment of prediction accuracy of SVM to optimize the solution results, faster to get the best approximate solution. This research uses previous literature 70 sets of data. Through analysis of the input data Health, Safety, Environment , Ergonomic to predict employees of mental workload. The data is taken questionnaires collected. 70 sets of data into the document the proportion of three different, to get the best approximate solution, the results of three ways, Environment and ergonomics are important first two parameters, In particular, environmental factors determining the mental workload of the most important influencing factors. Its independent testing of prediction accuracy of up to 73.3%.