由於運動對人體生理參數的影響，一直是令人關心及討論的話題，加上生醫感測理論與相關硬體技術的發達，我們開始有能力對人體內部複雜的運作模式進行仿生模擬與研究分析。故本論文的研究，利用相關的生醫感測器量測生理訊號，試圖探討各種不同強度的運動與人體生理參數的影響，試著建構出人體外在運動行為與內部參數相互對應的人體運動模型。本研究之主要目的試圖以人體生理參數，分別為心跳數與呼吸次數，建構出人體的運動模型，找出不同運動強度與生理參數變化的對應關係，探討心跳在持續一段時間內的變化，與不同運動強度的對應關係，做出人體運動時的系統識別。
針對系統識別相關理論用在人體運動模型上時，提出使用倒傳遞類神經網路與灰色理論兩種方法於建模上的實現人體運動模型，之後對於建模後的結果，做出分析與比較，並對估測運動程度的可能性做出最佳的探討。

The effects of exercise on human`s physiology parameter has always been the most attractive topic. Due to the advance of bio-medical theory and hardware, we can able to conduct bionic simulation on the complex human motion for study and analysis. Therefore this research is attempting to discuss the variety of human physiology parameter correspond to different intensity exercise by using the biosensors. Trying to construct the model about external movement and the correspond internal parameter of the human motion. The main object of the research is to find the correspondence between the human physiology parameter correspond to different intensity exercise, and the parameters are heart rate and breath rate. By constructing human motion model to find out the relationship between different intensity exercise and the variation of physiology parameters. Then study the variation of the heart rate in a period time due to different exercise intensity, conclude the system identification of human motion.
In regard to the system identification correlation theories when the human motion model, proposed that use back-propagation network and the grey theory two methods realize the human motion model in the modelling, afterward after the modelling result, makes analyzes and compares, and makes the best discussion.

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