過勞警示系統開發與評估

Development and evaluation of overstrain warning system

蕭毅昊(Yi-Hao Hsiao)

指導教授 ： 皮世明

中原大學/商學院/資訊管理研究所/碩士(2018年)

https://doi.org/10.6840/cycu201800300

摘要

近年來，因工作壓力導致過勞傷病、失能及死亡事件陸續發生，這也使得國內外對於職業安全的意識逐漸抬頭。目前市場上對於疲勞的測定方法大多過於繁雜，工作者無法清楚了解自身疲勞累積之程度。本研究之研究目的，在於簡化過勞之量測方法，以及為使用者設計過勞之警示機制。透過系統雛型開發方法，以智慧型手機搭配穿戴式裝置，建置一套過勞警示系統。針對受測者收集四週之過勞相關資料，並據此建立決策樹與灰預測模型，分析過勞相關行為因子。研究結果發現每位受測者之過勞行為因子皆有差異。同時，在過勞警示系統、職場過勞問卷與過勞預防系統三個過勞檢測系統中，透過有用性及易用性之評估，結果發現過勞警示系統相較於其他兩者有較好之表現。因此，本研究的研究貢獻在於提供使用者一套簡便之過勞檢測模型，並利用此模型建立警示機制，以協助使用者監控自身的過勞狀態。

關鍵字

過勞 ； 穿戴式裝置 ； 決策樹 ； 灰預測

並列摘要

In recent years, overstrain injuries, illnesses and deaths have occurred one after another as a result of work stress, which has also raised the awareness of occupational safety at home and abroad. At present, most of the methods for determining fatigue in the market are too complicated, and workers cannot clearly understand the degree of fatigue accumulation. The research purpose of this research is to simplify the measurement method of overstrain and to design a warning mechanism for overstrain. Through the system prototype development method, a wearable device is paired with a smart phone, and an overstrained warning system is built. Four-week overstrain related data was collected for the subjects, and a decision tree and gray prediction model was established based on this, and the related behavior factors related to labor were analyzed. The results of the study found that the behavioral factors of overstrain for each subject were different. At the same time, through the assessment of usefulness and ease of use in the three Overstrain inspection systems for the overstrain warning system, the Chinese Burnout Inventory and the overstrain prevention system, it was found that the overstrain warning system was better than the other two. Performance. Therefore, the research contribution of this study is to provide users with a simple overstrain detection model and use this type of alert mechanism to help users monitor their own overstrain status.

並列關鍵字

Overstrain ； Wearable Device ； Decision Tree ； Grey Prediction

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