在現實的社會中有很多事物的分類界限是不明確的，例如，醫學、生化指標。因此必須有一個明確的工具來處理不明確的界限。而量測出來的值應如何歸屬，模糊理論正提供了一個有系統的架構來處理不明確的界限。Amaya與Beliakov（1995）提出了如何建構多數人能了解的模糊隸屬函數和發展了一個讓隸屬函數進化的方法。本研究以Amaya與Beliakov（1995）的模型為基礎，試著引入模糊理論以評定運動負荷之「各種生化指標所量測出來的值」的決斷工具。然而，有些時候各領域的專家們會認為這樣的方法缺乏統計資料佐助，亦有因自身經驗累積的自信心，讓他們無法完全接受這樣的隸屬函數建構法。因此，未來在隸屬函數進化的過程中，透過一些方法進而對隸屬函數作精緻化的修改。

In real world domains, such as medicine, biochemical index is by nature imprecise. As a consequence, the expert systems oriented to these domains must have specific tools to deal with the uncertainty. How to interpret the results of laboratory tests (we refer to the area of exercise intensity diagnostics). The theory of fuzzy sets provides a systematic framework for dealing with fuzzy quantifiers. Amaya and Beliakov (1995) showed how to build the membership function for the intersubject fuzziness and presented a method for interactive refinement of the obtained curves. We follow the Amaya and Beliakov's (1995) model to employ the same inference engine used to deal with the imprecise information provided by measuring the physiological and biochemical data of athletes.However, in some cases the derived curves have not been accepted by the experts. This could be due to lack of statistical data or experience. Therefore, the further process requires interactive refinement of the corresponding possibility distribution.