使用人體輪廓資訊與kNN分類器的即時跌倒偵測系統

Abstract

監視系統在人機互動( Human Computer Interaction )之領域上為一項重要的研究，在未來社會高齡化的情形將日趨嚴重的同時，隨之而來的看護人力成本將大量上升，因此許多國內外的學者都致力於老人看護監視之研究上，以期輔助現有的人力看護系統，有效的降低龐大的人力支出成本。本研究使用並整合本實驗室所開發之人臉偵測系統用於追蹤人體並得到人體的特徵，利用kNN( k-th Nearest Neighbor )分類法分類人類姿勢並經由實驗統計所得之速度資訊來實作跌倒偵測系統。

In the province of Human Computer Interaction, monitor system is an important study. As long as the situation of aging society becomes more and more serious, the care costs will increase plenty. That is the reason so many domestic and foreign scholars throw themselves into the research of elderly care monitor system in order to support the existing care system and reduce the huge expenditures of labor costs. This research used and integrated the human face detection system developed by our laboratory to get the characteristics of the human body and track that. And also used k-th Nearest Neighbor classification to classify the human postures. Then using the information of the changing rate collected by many experiments this research finally can develop a fall detection system.