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We propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the(More)
The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000-2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling(More)
A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying; thus, the unmixing filters should also be time varying, which are difficult to calculate in real time. In the proposed approach, the visual modality is utilized(More)
We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person's normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible(More)
In this paper, we propose a novel and robust fall detection system by using a one class support vector machine based on video information. Video features, including the differences of centroid position and orientation of a voxel person over a time interval are extracted from multiple cameras. A one class support vector machine (OCSVM) is used to distinguish(More)
In the paper, we propose a fall detection method based on head tracking within a smart home environment equipped with video cameras. A motion history image and code-book background subtraction are combined to determine whether large movement occurs within the scene. Based on the magnitude of the movement information, particle filters with different state(More)
In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then(More)
In this paper, we propose an efficient and robust fall detection system by using a fuzzy one class support vector machine based on video information. Two cameras are used to capture the video frames from which the features are extracted. A fuzzy one class support vector machine (FOCSVM) is used to distinguish falling from other activities, such as walking,(More)
We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as(More)
Alkanethiol self-assembled monolayer (SAM) phases on Au(111) have been assumed to involve direct S head group bonding to the substrate. Using x-ray standing wave experiments, we show the thiolate actually bonds to gold adatoms; self-organization in these archetypal SAM systems must therefore be governed by the movement of these Au-S-R moieties on the(More)