Robust detection of events and activities, such as falling, sitting and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring extends to wherever the subject may go. Furthermore, since the captured frames are not of the subject, privacy is preserved. We present an improved fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. Trials were performed on five different subjects wearing a camera on their waist, each performing 40 different activities. Experimental results show the success of the proposed method.