The Transmission Control Protocol.

This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.

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Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications.

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intrasession authentication, 2%-3% for intersession authentication, and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.

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Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

Wearable Sensors for Remote Health Monitoring.

A survey on privacy in mobile participatory sensing applications

The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.

Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

Over the last years, there has been an increasing research interest in the application of accelerometry data for many kinds of automated gait analysis algorithms. The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose an improved biometric gait recognition approach with a stable cycle detection mechanism and comparison algorithm. Unlike previous work on wearable gait recognition, which was based from simple average cycling methods to more complicated methods, this paper reports new techniques for which can improve the performance, by using simple approaches. Preprocessing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 60 volunteers and 12 sessions each volunteer and resulted in an equal error rate (EER) of 5.7%.

Improved Cycle Detection for Accelerometer Based Gait Authentication

Gait and activity recognition using commercial phones

Mobile phones with a camera function are capable of capturing image and processing tasks. Fingerprint recognition has been used in many different applications where high security is required. A first step towards a novel biometric authentication approach applying cell phone cameras capturing fingerprint images as biometric traits is proposed. The proposed method is evaluated using 1320 fingerprint images from each embedded capturing device. Fingerprints are collected by a Nokia N95 and a HTC Desire. The overall results of this approach show a biometric performance with an Equal Error Rate (EER) of 4.5% by applying a commercial extractor/comparator and without any preproccesing on the images.

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Fingerprint Recognition with Embedded Cameras on Mobile Phones

The goal of our research is to develop methods for accelerometer-based gait recognition, which are robust, stable and fast enough to be used for authentication on mobile devices. To show how far we are in reaching this goal we developed a new cycle extraction method, implemented an application for android phones and conducted a scenario test. We evaluated two different methods, which apply the same cycle extraction technique but use different comparison methods. 48 subjects took part in the scenario test. After enrolment they were walking for about 15 minutes on a predefined route. To get a realistic scenario this route included climbing of stairs, opening doors, walking around corners etc. About every 30 seconds the subject stopped and the authentication was started. This paper introduces the new cycle extraction method and shows the Detection Error Trade-Off-curves, error rates separated by route-section and subject as well as the computation times for enrolment and authentication on a Motorola milestone phone.

Mohammad Derawi

Papers

Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

Improved Cycle Detection for Accelerometer Based Gait Authentication

Gait and activity recognition using commercial phones

Fingerprint Recognition with Embedded Cameras on Mobile Phones

Scenario test of accelerometer-based biometric gait recognition