Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Ni et al. <s> Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events. ::: ::: To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Ni et al. <s> In this work, we develop a computer vision based fall prevention system for hospital ward application. To prevent potential falls, once the event of patient get up from the bed is automatically detected, nursing staffs are alarmed immediately for assistance. For the detection task, we use a RGBD sensor (Microsoft Kinect). The geometric prior knowledge is exploited by identifying a set of task-specific feature channels, e.g., regions of interest. Extensive motion and shape features from both color and depth image sequences are extracted. Features from multiple modalities and channels are fused via a multiple kernel learning framework for training the event detector. Experimental results demonstrate the high accuracy and efficiency achieved by the proposed system. <s> BIB002
The majority of the patients that suffer a fall while in the hospital, tried to either suddenly get up from their bed or move from the bed to a chair BIB002 . Ni et al. designed a computer-vision-based system to prevent in-hospital patients from falling from their beds. This system performs event detection, specifically, detecting when a person gets up from a bed. The idea behind this study is to alert the nursing staff when the patient gets up. In this way, nurses can immediately assist him/her and avoid any fall. A Microsoft kinect was used to capture the images of the patients. The camera is mounted to a bed from the side view. A specific region of interest was used (the bed area), that was further divided into 4 × 2 equally sized rectangular boxes, which were called channels. A multiple kernel learning framework was employed to extract the features from the channels. A leave-one-subject-out testing scheme was utilized to measure the performance of the system. The system was compared with a state-of-the-art recognition system (STIPs BIB001 ), which was outperformed by its results.