Smart wearable systems: Current status and future challenges

Pervasive healthcare systems, smart grids, and unmanned aircraft systems are examples of Cyber-Physical Systems (CPSs) that have become highly integrated in the modern world. As this integration deepens, the importance of securing these systems increases. In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. We summarize advantages and drawbacks of each dimension’s options. We also summarize the most and least studied CPS IDS techniques in the literature and provide insight on the effectiveness of IDS techniques as they apply to CPSs. Finally, we identify gaps in CPS IDS research and suggest future research areas.

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A survey of intrusion detection techniques for cyber-physical systems

Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.

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Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.

Video-Based Abnormal Human Behavior Recognition—A Review

Increasing trend of wearables and multimodal interface for human activity monitoring: A review.

Digital cities of the future: Extending @home assistive technologies for the elderly and the disabled

Sensor networks are often used to monitor sensitive information from the environment or track sensitive objects' movements. Anonymity has become an important problem in sensor networks, and has been widely researched in wireless ad hoc and wired networks. The limited capacity and resources of current sensor networks have brought new challenges to anonymity research. In this paper, two efficient methods are proposed based on using a one-way hash chain to dynamically change the identity of sensor nodes in order to provide anonymity, and their anonymity properties are analyzed and compared.

Providing Anonymity in Wireless Sensor Networks

In recent years, there is a growing interest about assisted living environments especially for the elderly who live alone, due to the increasing number of aged people. In order for them to live safe and healthy, we need to detect abnormal behavior that may cause severe and emergent situations for the elderly. In this work, we suggest a method that detects abnormal behavior using wireless sensor networks. We model an episode that is a series of events, which includes spatial and temporal information about the subject being monitored. We define a similarity scoring function that compares two episodes taking into consideration temporal aspects. We propose a way to determine a threshold to divide episodes into two groups that reduces wrong classification. Weights on individual functions that consist the similarity function are determined experimentally so that they can produce the good results in terms of area under curve in receiver operating characteristic (ROC) curve.

Abnormal human behavioral pattern detection in assisted living environments

Sensor networks may be used in many monitoring applications where the locations of the monitored objects are quite sensitive and need to be protected. Previous research mainly focuses on protecting source location against mote-class attackers who only have a local view of the network traffic. In this paper, we focus on how to protect the source location against laptop-class attackers who have a global view of the network traffic. This paper proposes four schemes---naive, global, greedy, and probabilistic---to deal with laptop-class attacks. The naive solution uses maintenance messages sent periodically to hide real event reports. The global and greedy solutions improve the naive solution by reducing the latency of event delivery without increasing communication overhead. The probabilistic solution further improves the performance by reducing communication overhead without sacrificing location privacy. Experiments show that the probabilistic solution is practical for providing source location privacy against a laptop-class attacker.

Source location privacy against laptop-class attacks in sensor networks

As the world population is aging, there is an increasing need to support independent living of elderly people. Assistive environments incorporate the latest pervasive and ubiquitous technologies and provide a viable alternative to traditional assistive living. In this paper, we propose an evaluation framework to assess the quality of assistive environments. An assistive environment can be successful only if the potential users are willing to adopt it. The proposed framework identifies a set of attributes that are considered critical to user adoption. Sample metrics, as well as possible approaches to measure them, are also suggested to quantify those attributes. The framework is illustrated using an experimental assistive apartment environment that is being built at the University of Texas at Arlington.

/pdf/towards-an-evaluation-framework-for-assistive-environments-1a40omxknr.pdf

Zhengyi Le

Papers

Digital cities of the future: Extending @home assistive technologies for the elderly and the disabled

Providing Anonymity in Wireless Sensor Networks

Abnormal human behavioral pattern detection in assisted living environments

Source location privacy against laptop-class attacks in sensor networks

Towards an evaluation framework for assistive environments