Smart Camera Based Monitoring System and Its Application to Assisted Living

ArticleinProceedings of the IEEE 96(10):1698 - 1714 · November 2008with58 Reads
Impact Factor: 4.93 · DOI: 10.1109/JPROC.2008.928765 · Source: IEEE Xplore

Western societies are aging rapidly. An automated 24/7 surveillance to ensure safety of the elderly while respecting privacy becomes a major challenge. At the same time this is representative of novel and emerging video surveillance applications discovered lately besides the classic surveillance protection applications in airports, government buildings, and industrial plants. Three problems of current surveillance systems are identified. A distributed and automated smart camera based approach is proposed that addresses these problems. The proposed system's goal set is to analyze the real world and reflect all relevant-and only relevant-information live in an integrated virtual counterpart for visualization. It covers georeferenced person tracking and activity recognition (falling person detection). A prototype system installed in a home for assisted living has been running 24/7 for several months now and shows quite promising performance.

    • "A complete security architecture for patient or elderly monitoring use cases is not particularly addressed in the previous literature. Although9101112 specifically describe the VSNs in the context of assisted living and elderly care, they do not provide sufficient solutions for the security vulnerabilities, as discussed in [1]. A multitude of research on WBAN security and privacy already exists in the literature [13,14]. "
    [Show abstract] [Hide abstract] ABSTRACT: Video surveillance is widely deployed for many kinds of monitoring applications in healthcare and assisted living systems. Security and privacy are two promising factors that align the quality and validity of video surveillance systems with the caliber of patient monitoring applications. In this paper, we propose a symmetric key-based security framework for the reactive video surveillance of patients based on the inputs coming from data measured by a wireless body area network attached to the human body. Only authenticated patients are able to activate the video cameras, whereas the patient and authorized people can consult the video data. User and location privacy are at each moment guaranteed for the patient. A tradeoff between security and quality of service is defined in order to ensure that the surveillance system gets activated even in emergency situations. In addition, the solution includes resistance against tampering with the device on the patient’s side.
    Full-text · Article · Jan 2016 · Sensors
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    • "The computational power has been increased while, at the same time, costs have been reduced. Furthermore , computer vision advances have given video cameras the ability of 'seeing', becoming smart cameras (Fleck & Strasser, 2008). This has enabled the development of vision-based intelligent monitoring systems that are able to automatically extract useful information from visual data to analyse actions, activities and behaviours (Chaaraoui et al., 2012b), both for individuals and crowds, monitoring, recording and indexing video bitstreams (Tian et al., 2008). "
    [Show abstract] [Hide abstract] ABSTRACT: Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.
    Full-text · Article · Jun 2015 · Expert Systems with Applications
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    • "However, vision systems have privacy concerns and wearable sensors have compliance challenges, especially in patients with dementia. For more information about pros and cons of these systems see5678. Fall detection through floor vibration is considered to be a very new approach in comparison to aforementioned methods. "
    [Show abstract] [Hide abstract] ABSTRACT: Activity monitoring is one of the predominant concerns of the elderly living at home. For example, serious complications or even death can be avoided if the elder is helped shortly after a serious fall. Several methods have been proposed to recognize human activity. Most of them can be categorized in three general groups: (1) vision-based methods; (2) wearable sensors; and (3) vibration-based methods. Despite the advantages of vision-based methods and wearable sensors, privacy concerns in the first type, and compliance challenges in the second group motivate the health care industry to pay attention to other types of environmental monitoring techniques such as vibration-based methods. Vibration-based methods use accelerometers placed at the floor of the patient’s dwelling. These methods are appealing because accelerometers are not expensive and installation is easy. The classification of events is critical in vibration-based methods. This paper proposes a classification algorithm using Multinomial Logistic Regression (MLR). A data set of different human activities is experimentally obtained. Some signals are used to train the classification algorithm while the remaining records are used to test the proposed technique. The signal “peak” and a “proposed time index” are used for classification. Principal component analysis is used to reduce the number of parameters considered for MLR given that each event was captured by four sensors. The main advantage of this classification algorithm is its probabilistic nature. Results show that in most cases, the event was correctly identified by the group having the highest probability. However, other signal characteristics should be explored to improve the performance of the proposed technique.
    No preview · Chapter · Jan 2015
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