3,516 research outputs found
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Orientation Invariant ECG-Based Stethoscope Tracking for Heart Auscultation Training on Augmented Standardized Patients
Auscultation, the act of listening to the heart and lung sounds, can reveal substantial information about patients’ health and other cardiac-related problems; therefore, competent training can be a key for accurate and reliable diagnosis. Standardized patients (SPs), who are healthy individuals trained to portray real patients, have been extensively used for such training and other medical teaching techniques; however, the range of symptoms and conditions they can simulate remains limited since they are only patient actors. In this work, we describe a novel tracking method for placing virtual symptoms in correct auscultation areas based on recorded ECG signals with various stethoscope diaphragm orientations; this augmented reality simulation would extend the capabilities of SPs and allow medical trainees to hear abnormal heart and lung sounds in a normal SP. ECG signals recorded from two different SPs over a wide range of stethoscope diaphragm orientations were processed and analyzed to accurately distinguish four different heart auscultation areas, aortic, mitral, pulmonic and tricuspid, for any stethoscope’s orientation. After processing the signals and extracting relevant features, different classifiers were applied for assessment of the proposed method; 95.1% and 87.1% accuracy were obtained for SP1 and SP2, respectively. The proposed system provides an efficient, non-invasive, and cost efficient method for training medical practitioners on heart auscultation
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
Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)
This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981
Proceedings, MSVSCC 2019
Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019.
The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference.
Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference.
Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere
Deep Learning Based Malware Classification Using Deep Residual Network
The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB
From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices
This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)
Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review
Animals play a profoundly important and intricate role in our lives today.
Dogs have been human companions for thousands of years, but they now work
closely with us to assist the disabled, and in combat and search and rescue
situations. Farm animals are a critical part of the global food supply chain,
and there is increasing consumer interest in organically fed and humanely
raised livestock, and how it impacts our health and environmental footprint.
Wild animals are threatened with extinction by human induced factors, and
shrinking and compromised habitat. This review sets the goal to systematically
survey the existing literature in smart computing and sensing technologies for
domestic, farm and wild animal welfare. We use the notion of \emph{animal
welfare} in broad terms, to review the technologies for assessing whether
animals are healthy, free of pain and suffering, and also positively stimulated
in their environment. Also the notion of \emph{smart computing and sensing} is
used in broad terms, to refer to computing and sensing systems that are not
isolated but interconnected with communication networks, and capable of remote
data collection, processing, exchange and analysis. We review smart
technologies for domestic animals, indoor and outdoor animal farming, as well
as animals in the wild and zoos. The findings of this review are expected to
motivate future research and contribute to data, information and communication
management as well as policy for animal welfare
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