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https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/34966
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| Title: | 使用RFID與機器學習技術於獨自在家老人的異常行為偵測 |
| Other Titles: | Abnormal behavior detection for the home-alone elderly by RFID and machine learning |
| Authors: | 陳建丞;Chen, Chien-chen |
| Contributors: | 淡江大學資訊工程學系碩士班
許輝煌;Hsu, Hui-huang |
| Keywords: | RFID;RSSI;老人照護;機器學習;行為模式分析;群集分析;RFID;RSSI;elderly care;Machine learning;behavior analysis;Cluster Analysis |
| Date: | 2009 |
| Issue Date: | 2010-01-11 05:49:58 (UTC+8) |
| Abstract: | 在這個研究中，我們假設老人是獨自生活在一個環境裡，我們利用RFID的技術來偵測老人是否有異常的行為。RFID技術的基本架構是由Reader去讀取Tag，而Reader在讀取Tags訊號時，因為訊號強弱以及距離遠近的關係，我們可以獲得Tag訊號的強弱值(RSSI值 0~255)。
我們希望以RSSI值的數據來判斷和記錄長輩平日在家移動的數據資料，我們計畫的RFID系統架構是我們在生活的環境中佈滿許多
Tags，例如客廳角落放置、餐廳、廁所、臥房……等等，而Reader則由老人隨身攜帶，跟隨著老人日常生活習慣去移動，亦即時記錄所讀取到的RSSI值，在藉由機器學習的技術來建立一個“正常”行為的模型，進而用來判斷其後續是否屬於異常，若是異常，則發出警訊。
這樣的方法，不同於許多其他的以電腦視覺為主的方法，不僅僅是偵測老人是否跌倒而已，藉由平常紀錄的RSSI值，可以找到老人的各種生活模式，除了跌倒，是否有午睡習慣、上廁所頻率、開冰箱的
紀錄……等等。我們都可以利用機器學習的方法學習並判斷。另外，我們利用RSSI值的這個方法，並不像一些偵測跌倒異常的偵測技術必須要利用到攝影機，更是減低了一般人對隱私權的疑慮。
而最後的實驗結果中，有幾筆測試資料的結果非常好，也足以證明我們利用群集分析來判斷異常行為這樣的想法與理論的確是可以行的通，雖然我們並無法100%可以將異常生活行為模式的資料點給找出來，但我們所做的群集分析結果，對於一個老人的行為分析，確實可以提供相當程度的參考價值。在未來的研究方向中，或許我們可以再使用許多更精密的群集分析技術來提升我們偵測異常行為的準確率。
In this research, we aim at building an intelligent system that can detect abnormal behavior for the home-alone elderly at home. Deployment of RFID tags at home helps us collect the daily movement data of the elderly. The RFID technology uses a reader to detect tags. When the reader detects the signals from the tags, RSSI values (0-255) that represent signal strength can be obtained.
We want to build a behavior model (viewed as normal) for an elderly person by a set of long-term collected RSSI data. We then can use this model to detect subsequent “abnormal” behavior of the person. The system architecture includes deploying active tags in the living environment, e.g., the living room, the dining room, the kitchen, the rest room, and the bed rooms. The reader is to be carried by an elderly person. The detected RSSI values are recorded following the movement of the person. The behavior model built by machine learning can be used to determine if the subsequent behavior is normal or not. Since only data of “normal” behavior are collected, the model (a classifier) can be build by only positive examples.
This approach, different from computer-vision-based approaches, not just detects predefined events like if the elderly person falls, but finds the living patterns of the person. The machine learning technique can be used to learn all the patterns without defining all of them in advance. Also, it is not necessary to install cameras at home. This can relieve the concern of personal privacy issues. Researches related to using RFID or other sensors for elderly care can also be found, but all of them do not focus on behavior modeling as we do.
In the experiments, several test results are very successful. This is enough to prove that utilizing clustering analysis here is really practical. Though our system cannot perfectly detect abnormal behavior, it certainly provides valuable information to the analysis of human behavior at home. In the future, using more sophisticated clustering techniques should to able to improve the detection of abnormal behavior. |
| Appears in Collections: | [資訊工程學系暨研究所] 學位論文
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