Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Comparative analysis and fusion of spatiotemporal information for footstep recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Vera-Rodriguez, J. S. D. Mason, J. Fierrez, and J. Ortega-Garcia, "Comparative analysis and fusion of spatiotemporal information for footstep recognition", Pattern Analysis and Machine Intelligence, IEEE Transaction, vol. 35, no. 4, pp. 823-834, August 2012Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.Ruben Vera-Rodriguez, Julian Fierrez and Javier Ortega Garcia are supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), TeraSense (CSD2008-00068) and ‘Catedra UAM-Telefonica’

Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques

As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out data belonging to non-residents. Gait related information belonging exclusively to the older residents can then be gathered. The data can potentially help detect changes in gait patterns which can be used to analyze fall risk for elderly residents by passively observing them in their home environments