23,818 research outputs found
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
On the Unicity of Smartphone Applications
Prior works have shown that the list of apps installed by a user reveal a lot
about user interests and behavior. These works rely on the semantics of the
installed apps and show that various user traits could be learnt automatically
using off-the-shelf machine-learning techniques. In this work, we focus on the
re-identifiability issue and thoroughly study the unicity of smartphone apps on
a dataset containing 54,893 Android users collected over a period of 7 months.
Our study finds that any 4 apps installed by a user are enough (more than 95%
times) for the re-identification of the user in our dataset. As the complete
list of installed apps is unique for 99% of the users in our dataset, it can be
easily used to track/profile the users by a service such as Twitter that has
access to the whole list of installed apps of users. As our analyzed dataset is
small as compared to the total population of Android users, we also study how
unicity would vary with larger datasets. This work emphasizes the need of
better privacy guards against collection, use and release of the list of
installed apps.Comment: 10 pages, 9 Figures, Appeared at ACM CCS Workshop on Privacy in
Electronic Society (WPES) 201
Knowing Your Population: Privacy-Sensitive Mining of Massive Data
Location and mobility patterns of individuals are important to environmental
planning, societal resilience, public health, and a host of commercial
applications. Mining telecommunication traffic and transactions data for such
purposes is controversial, in particular raising issues of privacy. However,
our hypothesis is that privacy-sensitive uses are possible and often beneficial
enough to warrant considerable research and development efforts. Our work
contends that peoples behavior can yield patterns of both significant
commercial, and research, value. For such purposes, methods and algorithms for
mining telecommunication data to extract commonly used routes and locations,
articulated through time-geographical constructs, are described in a case study
within the area of transportation planning and analysis. From the outset, these
were designed to balance the privacy of subscribers and the added value of
mobility patterns derived from their mobile communication traffic and
transactions data. Our work directly contrasts the current, commonly held
notion that value can only be added to services by directly monitoring the
behavior of individuals, such as in current attempts at location-based
services. We position our work within relevant legal frameworks for privacy and
data protection, and show that our methods comply with such requirements and
also follow best-practice
Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations
Gait velocity has been consistently shown to be an important indicator and
predictor of health status, especially in older adults. It is often assessed
clinically, but the assessments occur infrequently and do not allow optimal
detection of key health changes when they occur. In this paper, we show that
the time gap between activations of a pair of Passive Infrared (PIR) motion
sensors installed in the consecutively visited room pair carry rich latent
information about a person's gait velocity. We name this time gap transition
time and show that despite a six second refractory period of the PIR sensors,
transition time can be used to obtain an accurate representation of gait
velocity.
Using a Support Vector Regression (SVR) approach to model the relationship
between transition time and gait velocity, we show that gait velocity can be
estimated with an average error less than 2.5 cm/sec. This is demonstrated with
data collected over a 5 year period from 74 older adults monitored in their own
homes.
This method is simple and cost effective and has advantages over competing
approaches such as: obtaining 20 to 100x more gait velocity measurements per
day and offering the fusion of location-specific information with time stamped
gait estimates. These advantages allow stable estimates of gait parameters
(maximum or average speed, variability) at shorter time scales than current
approaches. This also provides a pervasive in-home method for context-aware
gait velocity sensing that allows for monitoring of gait trajectories in space
and time
ILR Research in Progress 2006-07
The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.Research_in_Progress_2006_07.pdf: 18 downloads, before Oct. 1, 2020
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
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