5,863 research outputs found
How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers
Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Detection of groups of interacting people is a very interesting and useful
task in many modern technologies, with application fields spanning from
video-surveillance to social robotics. In this paper we first furnish a
rigorous definition of group considering the background of the social sciences:
this allows us to specify many kinds of group, so far neglected in the Computer
Vision literature. On top of this taxonomy, we present a detailed state of the
art on the group detection algorithms. Then, as a main contribution, we present
a brand new method for the automatic detection of groups in still images, which
is based on a graph-cuts framework for clustering individuals; in particular we
are able to codify in a computational sense the sociological definition of
F-formation, that is very useful to encode a group having only proxemic
information: position and orientation of people. We call the proposed method
Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all
the state of the art methods in terms of different accuracy measures (some of
them are brand new), demonstrating also a strong robustness to noise and
versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On
A Cooperative Emergency Navigation Framework using Mobile Cloud Computing
The use of wireless sensor networks (WSNs) for emergency navigation systems
suffer disadvantages such as limited computing capacity, restricted battery
power and high likelihood of malfunction due to the harsh physical environment.
By making use of the powerful sensing ability of smart phones, this paper
presents a cloud-enabled emergency navigation framework to guide evacuees in a
coordinated manner and improve the reliability and resilience in both
communication and localization. By using social potential fields (SPF),
evacuees form clusters during an evacuation process and are directed to
egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm.
Rather than just rely on the conventional telecommunications infrastructures,
we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong
the life time of smart phones, that adaptively searches optimal communication
routes between portable devices and the egress node that provides access to a
cloud server with respect to the remaining battery power of smart phones and
the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by
ISCIS 2014 (29th International Symposium on Computer and Information
Sciences
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Tracking Human Mobility using WiFi signals
We study six months of human mobility data, including WiFi and GPS traces
recorded with high temporal resolution, and find that time series of WiFi scans
contain a strong latent location signal. In fact, due to inherent stability and
low entropy of human mobility, it is possible to assign location to WiFi access
points based on a very small number of GPS samples and then use these access
points as location beacons. Using just one GPS observation per day per person
allows us to estimate the location of, and subsequently use, WiFi access points
to account for 80\% of mobility across a population. These results reveal a
great opportunity for using ubiquitous WiFi routers for high-resolution outdoor
positioning, but also significant privacy implications of such side-channel
location tracking
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