Elena Vildjiounaite

Identifying users of portable devices from gait pattern with accelerometers

Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7%) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10%, 18% and 19%, respectively.

Identifying people from gait pattern with accelerometers

Protecting portable devices is becoming more important, not only because of the value of the devices themselves, but for the value of the data in them and their capability for transactions, including m-commerce and m-banking. An unobtrusive and natural method for identifying the carrier of portable devices is presented. The method uses acceleration signals produced by sensors embedded in the portable device. When the user carries the device, the acceleration signal is compared with the stored template signal. The method consists of finding individual steps, normalizing and averaging them, aligning them with the template and computing cross-correlation, which is used as a measure of similarity. Equal Error Rate of 6.4% is achieved in tentative experiments with 36 test subjects.

Privacy, identity and security in ambient intelligence: a scenario analysis

The success of ambient intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This article addresses these issues by analysing scenarios for ambient intelligence applications that have been developed over the last few years. It elaborates the assumptions that promotors make about the likely use of the technology and possibly unwanted side effects. It concludes with a number of threats for personal privacy that become evident.

Soft biometrics-combining body weight and fat measurements with fingerprint biometrics

The aim of this study was to examine whether using soft biometrics, i.e. easily measurable personal characteristics, such as weight and fat percentage, can improve the performance of biometrics in verification type applications. Fusing fingerprint biometrics with soft biometrics, in this case body weight measurements, decreased the total error rate (TER) from 3.9% to 1.5% in an experiment with 62 test subjects. This result shows that simple physiological measurements can be used to support biometric recognition. Furthermore, soft biometrics are unobtrusive, there is no risk of identity theft, the perception of the big-brother effect is small, the equipment needed is low-cost, and the methods are easy to understand. Soft biometrics alone are not suitable for security related applications, but they can be used for improving the performance of traditional biometrics. A potentially feasible use for soft biometrics may be found in non-security, convenience type cases, such as domestic applications.

Location Estimation Indoors by Means of Small Computing Power Devices, Accelerometers, Magnetic Sensors, and Map Knowledge

A distributed real-time system, based on wearable accelerometers and magnetic sensors, is proposed for location estimation and recognition of walking behaviors. Suitable for both outdoor and indoor navigation, the system is especially adjusted for irregular movements indoors. The algorithm, which demands only small computing resources, performs step detection and classification in the time domain, allowing the estimation of the size of each separate step independently. Since the system finds the users position relative to an initial position, it is intended to be supplemented with different types of absolute positioning information. Making use of map knowledge, as an easily available source of this information, is analyzed. The conclusion is drawn that referring to the locations of the corridors and stairways increases the positioning accuracy and reduces the effect of magnetic field distortions encountered inside buildings. The positioning error of different system configurations was 3-10 % from traveled distance.

Five-point acceleration sensing wireless body area network - design and practical experiences

An implementation of an unobtrusive five-point acceleration sensing wireless body area network (WBAN) with mobile device data logging capabilities is presented, along with practical experiences on performance. Results show good communications performance in laboratory conditions. Field test performance is weaker and the reasons for this are discussed.

Unobtrusive Dynamic Modelling of TV Program Preferences in a Household

Majority of recommender systems require explicit user interaction (ranking of movies and TV programs and/or their metadata, such as genres, actors etc), which requires user time and effort. Furthermore, often such ranking is done separately by each person, while merging these manually acquired preferences in multi-user environments remains largely unsolved problem. This work presents a method to learn a model of multi-user environment in intelligent home from implicit interactions: the choices which family members make together and separately. In tests on TV viewing histories of twenty families, acquired during two months, the method has achieved prediction accuracy comparable with the accuracy of systems which require explicit user ratings: a set of TV programs, actually viewed during each test session (average set size was 2.2 programs per viewing session), was recommended among five top choices in 60% of cases on average, despite training on small data sets.

Increasing Security of Mobile Devices by Decreasing User Effort in Verification

Reliable user verification is important for security of computers and personal devices; however, most of well-performing verification methods require explicit user effort. As a consequence, an access is granted for a long time after the only successful verification, which allows replacing the authorized user to the advantage of an impostor, as it is often the case with mobile phones. This work proposes a method of frequent user verification, based on cascading of unobtrusive biometrics with more reliable biometrics, provided explicitly, in such a way that explicit effort is required only if unobtrusive verification fails. Experiments with voice, gait and fingerprint data have shown that in most of noise conditions cascade was able to satisfy security requirements of False Accept Rate 1% and to achieve overall False Reject Rate 3% or less, while requiring explicit effort in 10 - 60% of cases.

Unobtrusive user identification with light biometrics

Biometric methods are used for recognition and verification of the identity of a person in many applications. Certain concerns over the obtrusive nature of their use, threats to privacy and even the danger of identity theft are rising. In this paper unobtrusive and privacy preserving light biometrics, such as height, weight, and body fat percentage are suggested for user identification. An experiment with 62 test subjects was conducted. In verification type of application total error rate of 11% was achieved using weight data alone and fusion with height data reduced the error rate to 2.4%. With a short list of five best scoring identities the percentage of cases with the correct identity on the list was 90% for weight alone and 100% for the combination of weight and height. The application domain for light biometrics is seen in non-security applications, such as homes, small offices and health clubs.

The brave new world of ambient intelligence: an analysis of scenarios regarding privacy, identity and security issues

The success of Ambient Intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This contribution presents an analysis of ambient intelligence scenarios, particularly in regard to AmIs impacts on and implications for individual privacy. The analysis draws on our review of more than 70 AmI projects, principally in Europe. It notes the visions as well as the specifics of typical AmI scenarios. Several conclusions can be drawn from the analysis, not least of which is that most AmI scenarios depict a rather too sunny view of our technological future. Finally, reference is made to the SWAMI project (Safeguards in a World of Ambient Intelligence) which, inter alia, has constructed ”dark” scenarios, as we term them, to show how things can go wrong in AmI and where safeguards are needed.