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Automated monitoring of activities of elderly is very important in the field of elderly healthcare. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like walking, sitting and lying down can provide valuable information for early diagnosis of potential health problems. Recently, wearable smart cameras have emerged as a new area of research, since they provide several advantages. First, activity monitoring is not restricted to confined environments, where static sensors are installed. Second, privacy concerns of the person being monitored are alleviated. Recent works in the literature show promising results for fall detection. However, classification of activities like walking, sitting and lying down still remains as a challenge. Moreover, since most of the processing needs to be performed on board, it becomes imperative that the method has real-time capabilities. In this paper, we present a new and efficient method for activity classification using histogram of oriented gradients (HOG) and optical flow. Since the regular global optical flow methods can be inaccurate and computationally expensive, we use an alternative edge-based optical flow technique, which provides better speed and accuracy especially for the application and embedded platforms under consideration. A total of 195 experiments were conducted on eight subjects performing falling, sitting and lying down activities. Results demonstrate the promise and feasibility of and the speed up provided by the proposed method.