3,831 research outputs found
Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory
Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309
Forensic gait analysis â Morphometric assessment from surveillance footage
© 2019 Elsevier B.V. Following the technological rise of surveillance cameras and their subsequent proliferation in public places, the use of information gathered by such means for investigative and evaluative purposes sparked a large interest in the forensic community and within policing scenarios. In particular, it is suggested that analysis of the body, especially the assessment of gait characteristics, can provide useful information to aid the investigation. This paper discusses the influences upon gait to mitigate some of the limitations of surveillance footage, including those due to the varying anatomical differences between individuals. Furthermore, the differences between various techniques applied to assess gait are discussed, including biometric gait recognition, forensic gait analysis, tracking technology, and marker technology. This review article discusses the limitations of the current methods for assessment of gait; exposing gaps within the literature in regard to various influences impacting upon the gait cycle. Furthermore, it suggests a âmorphometricâ technique to enhance the available procedures to potentially facilitate the development of standardised protocols with supporting statistics and database. This in turn will provide meaningful information to forensic investigation, intelligence-gathering processes, and potentially as an additional method of forensic evaluation of evidence
Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric
Biometric techniques are often used as an extra security factor in
authenticating human users. Numerous biometrics have been proposed and
evaluated, each with its own set of benefits and pitfalls. Static biometrics
(such as fingerprints) are geared for discrete operation, to identify users,
which typically involves some user burden. Meanwhile, behavioral biometrics
(such as keystroke dynamics) are well suited for continuous, and sometimes more
unobtrusive, operation. One important application domain for biometrics is
deauthentication, a means of quickly detecting absence of a previously
authenticated user and immediately terminating that user's active secure
sessions. Deauthentication is crucial for mitigating so called Lunchtime
Attacks, whereby an insider adversary takes over (before any inactivity timeout
kicks in) authenticated state of a careless user who walks away from her
computer. Motivated primarily by the need for an unobtrusive and continuous
biometric to support effective deauthentication, we introduce PoPa, a new
hybrid biometric based on a human user's seated posture pattern. PoPa captures
a unique combination of physiological and behavioral traits. We describe a low
cost fully functioning prototype that involves an office chair instrumented
with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa
can be used in a typical workplace to provide continuous authentication (and
deauthentication) of users. We experimentally assess viability of PoPa in terms
of uniqueness by collecting and evaluating posture patterns of a cohort of
users. Results show that PoPa exhibits very low false positive, and even lower
false negative, rates. In particular, users can be identified with, on average,
91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several
prominent biometric based deauthentication techniques
Towards the text compression based feature extraction in high impedance fault detection
High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy background noise. In this paper, a new approach for partial discharge pattern recognition is presented. All results were obtained on data, acquired from real 22 kV medium voltage overhead power line with covered conductors. The proposed method is based on a text compression algorithm and it serves as a signal similarity estimation, applied for the first time on partial discharge pattern. Its relevancy is examined by three different variations of classification model. The improvement gained on an already deployed model proves its quality.Web of Science1211art. no. 214
From locomotion to dance and back : exploring rhythmic sensorimotor synchronization
Le rythme est un aspect important du mouvement et de la perception de lâenvironnement.
Lorsque lâon danse, la pulsation musicale induit une activitĂ© neurale oscillatoire qui permet au
systĂšme nerveux dâanticiper les Ă©vĂšnements musicaux Ă venir. Le systĂšme moteur peut alors sây
synchroniser.
Cette thĂšse dĂ©veloppe de nouvelles techniques dâinvestigation des rythmes neuraux non
strictement périodiques, tels que ceux qui régulent le tempo naturellement variable de la marche
ou la perception rythmes musicaux. Elle étudie des réponses neurales reflétant la discordance
entre ce que le systĂšme nerveux anticipe et ce quâil perçoit, et qui sont nĂ©cessaire pour adapter
la synchronisation de mouvements Ă un environnement variable. Elle montre aussi comment
lâactivitĂ© neurale Ă©voquĂ©e par un rythme musical complexe est renforcĂ©e par les mouvements qui
y sont synchronisĂ©s. Enfin, elle sâintĂ©resse Ă ces rythmes neuraux chez des patients ayant des
troubles de la marche ou de la conscience.Rhythms are central in human behaviours spanning from locomotion to music performance. In
dance, self-sustaining and dynamically adapting neural oscillations entrain to the regular auditory
inputs that is the musical beat. This entrainment leads to anticipation of forthcoming sensory
events, which in turn allows synchronization of movements to the perceived environment.
This dissertation develops novel technical approaches to investigate neural rhythms that are not
strictly periodic, such as naturally tempo-varying locomotion movements and rhythms of music.
It studies neural responses reflecting the discordance between what the nervous system
anticipates and the actual timing of events, and that are critical for synchronizing movements to
a changing environment. It also shows how the neural activity elicited by a musical rhythm is
shaped by how we move. Finally, it investigates such neural rhythms in patient with gait or
consciousness disorders
Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines
Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithmâthe soft margin constant C and the kernel function parameter âare investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults
Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis
The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPodâs inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures.
The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the âbench to the bedside.â This review only identified a few studies that explored ATâs potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-userâs perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system.
With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The studyâs analysis of the trunkâs vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a personâs gait cycle, ultimately permitting more clinically relevant gait features to be extracted.
Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunkâs anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances.
Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookesâ spin-off company âWildknowledgeâ, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments
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