Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Privacy <s> 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 is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Privacy <s> We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios. <s> BIB002
One of the main problems of camera-based systems is the fact that people do not like to be watched; they are worried that their intimacy will be violated. Few studies address this issue. The most common way researchers approach this issue is by only sending subjects' images when an alarm is triggered. Ozcan et al. BIB001 developed a fall detection system that utilizes a wearable embedded smart camera that is mounted on the waist of the subject of interest. The camera captures the images of the environment and not the subject's, eliminating the privacy concern. Gasparrini et al. BIB002 developed a fall detection system using a depth camera (Microsoft Kinect). Privacy issues are not a problem due to the fact that this cameras do not recognize the facial characteristics of the subject.