睡眠過程的行為，和個人健康有著密不可分的關係。因此本論文提出一個基於狀態轉移機制之多感測器睡眠行為偵測系統，用以偵測使用者睡眠過程的行為，包含床上的身體移動與睡眠姿勢、上下床、以及站立與走路。藉此不僅可紀錄使用者每日的睡眠行為，還可以協助使用者了解自我的睡眠情形，更可做為日後醫師診斷病症的輔助資訊。本系統採用多個接觸式加速度慣性感測器，同時提出一個狀態轉換的機制，用以表現動作與動作之間狀態轉移的關係；並導入一個能夠統計分析狀態轉移的數學機率模型，最後結合分類器以辨別使用者目前所處的狀態。本系統不僅能夠克服棉被遮蔽與昏暗環境的問題，也能解決使用者眾多且複雜的動作，以及考量動作與動作轉換之間的轉換機率與連續性，同時設備對使用者的睡眠影響也降至最低。實驗結果驗證本方法確實能夠對所定義的行為，做出正確且強健的偵測與辨識。

The body behavior of sleeper during night plays an important role for personal health. Because of that, we proposed a status-transition-based sleeper’s behavior detection system with multi-sensors to detect the body behavior during night, including body-turning and sleeping pose on bed, get out of bed, standing and walking. The detected results not only make user understand the daily behavior himself, but also can help doctor to diagnose the user’s disease. The proposed system adopted the contacted accelerometer sensors, and utilized a status transiting mechanism to describe the transited relation for each two statuses. And, the Markov Chain is applied to represent the transiting probability of statuses. Last, SVM is used to combine with transiting probability matrix to classify the sleeper’s behavior during night. The proposed system can easily face the occlusion caused by quilt and the dark background. At the same time, the continuous characteristic can also be considered. The experimental results show that the proposed system is robust and has a satisfied detecting rate for the defined behavior.

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