在高齡化的現代社會，獨居老人逐漸增加，居家照護就顯得相對的重要，。雖然在現今的科技中已經開發了許多數值型的檢測裝置來對於身體問題做明確的數值檢測，但是一個人的身體健康狀況單憑數值型的檢測裝置來評斷是不夠的，而且數值型裝置是無法檢查出存在於日常生活行為當中的隱性問題，日常生活行為異常所造成且無法靠硬體裝置所檢測出來的身體狀況。
本論文透過在家中已佈置好的感測Sensor所收集的行為資料來對獨自居住在家中的老年人的每天生活行為做隱性不規則的行為分析，藉由分群演算法基於機器學習中的無監督式學習方法，來對於居家老年人的日常生活行為間的關係做探討與以及異常行為日的分群，藉由特徵提取以及多維度轉換方式，分析出每天的行為間差異，用以檢測隱性不規則的異常行為日。

In the aging modern society, the number of elderly living alone has gradually increased, and home care has become relatively important. Although many numerical detection devices have been developed in today's technology to make explicit numerical tests for physical problems, it is not enough for a person's physical health to be judged by a numerical type of detection device alone, and the numerical device cannot Check out the hidden problems that exist in daily life behaviors, the physical conditions that are caused by abnormal behaviors in daily life and cannot be detected by hardware devices.
This paper makes a hidden and irregular behavior analysis of the daily life behavior of the elderly living alone in the home through the behavior data collected by the Sensor in the home. The group algorithm is based on the machine learning. Supervised learning method to explore the relationship between daily life behaviors of 
 
the elderly and the grouping of abnormal behavior days. Through feature extraction and multi-dimensional transformation, the daily behavioral differences are analyzed to detect recessiveness. Irregular abnormal behavior day.

目錄	IV
圖目錄	VI
第一章、簡介	1
第二章、相關研究	3
第三章、研究方法	8
3-1. 數據收集	8
3-2. 環境假設	8
3-3. 特徵提取	11
3-4. 多維度轉換	13
3-5. Null值填補	16
3-6. 距離計算	18
3-7. Hierarchical Clustering 	21
第四章、研究結果	28
4-1. 實驗結果	28
4-2. 實驗結果比較	31
第五章、結論與未來工作	34
參考文獻	35
附錄-英文論文	38 

圖1. 輸入資料格式	8
圖2. 每日行為字串轉換	10
圖3. 每日行為區段分隔	13
圖4. 多維度轉換	14
圖5. 多維度映射	15圖6 . MList	16
圖7 . MList 空值填補	19
圖8. 距離計算	21
圖9. 多維座標	25
圖10. 群集聚合	26
圖11.群集分群	26
圖12. Hierarchical Clustering	27
圖13. ABDT algorithm 10日行為資料實驗	29
圖14. ABDT algorithm 10日行為資料實驗分群圖	29
圖15. ABDT algorithm 365日行為資料實驗	30
圖16. ABDT algorithm 365日行為資料實驗分群圖	31
圖17. ABDT 分群圖	33
圖18. 360日分群準確率	33

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