目前用來量測個人移動距離的方法有兩大種，第一種:GPS的運用;第二種:計步器，但是利用GPS來計算移動距離的結果易受到外界環境影響，而計步器在短距離的誤差極大，因此本論文跳脫這兩種類型，改去探討目前還在研究的計算方式:利用感知器去取得移動時的訊號，再依靠這些訊號去計算出目前的移動距離。但是這種計算方式容易受到一些雜訊或是外在因素的影響，因此為了防止這些誤差，本論文先行辨識出當前運動，再依據個別的運動模式配合適當的計算方式，以提升準確率。

本論文的重點可分為三大部份，第一部分是使用慣性導航的去推估個人移動後的距離，第二部分藉由PDR的方式去分析量測訊號，再利用分析所得的變數去辨識人類在移動時常出現的運動模式，第三部分藉由第二部分辨識出的結論，對每一種運動模式建立適合的步伐模型，再進行相對應的實驗。

本論文使用加速儀、陀螺儀來分辨出當前的運動模式，再配合上各模式的步伐模型，可以減少許多雜訊干擾與相似運動的影響。若將其應用於室內運動，此種方式的估算出的距離準確率將會高於計步器的結果。

At present, there are two methods to measure the human moving distance. The first one is to use GPS. The second one is to use a pedometer. However, the data collected by using GPS to measure the moving distance is easily influenced by the external factors; besides, using pedometers to measure the short distance will have big error as well. As a result, this study will avoid the former two methods and aims at the new method which is still in research. This is to use the perceptron to get the moving signal, and then, based on those signals, to count out the moving distance. Nevertheless, this method is easily affected by eternal factors like miscellaneous signals. In order to decrease the error, this study will recognize the present mode of motions at first. Second, according to the individual motions mode, the researcher take the appropriate compute methods to increase the degree of accuracy.

There are three sections in this study. The first section is to use INS(Integrated Navigation System)to compute the individual moving distance. The second section is use PDR to analyze signals, and then use the parameter to recognize the motions mode which appears most often while people is moving. The third section is based on the conclusion of the second section to establish the appropriate model for each motions mode; and then, to do the experiment on those modes.

This study uses accelerometer and gyroscope to recognize the current motions mode, and match the diverse model for corresponding modes. In this way, it can decrease the influences of miscellaneous signals and similar motions modes. If applied to the indoor activities, this result of using this method will be much more accurate than the pedometer.

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