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Selected Projects in Biomedical Computing
Using Smartphones to Enable Interaction and Communication with
Autistic Children
Gondy Leroy, Claremont Graduate University; Gianluca De Leo, Old
Dominion University, U.S.
Autism spectrum disorder (ASD) is a developmental disorder that
afflicts more than 500,000 children in the United States and is
characterized by a wide variety of possible symptoms such as
developmental disabilities, extreme withdrawal, lack of social
behavior, severe language and attention deficits, and repetitive
behaviors. One of the primary impairments is difficulty with
communication: between a third to a half autism suffers do not have
functional verbal communication skills. It is estimated that as
many as 80% of children with autism below the age of 5 do not speak.
The goal of our system is to enable non-verbal autistic children to
speak using a mobile device. The project has two components: 1) the
software application for the handheld device used to compose
messages, track and review reports communication behavior, and 2) a
webpage that can be used by the parents or caregivers for formatting
personal images that are downloaded onto the handheld device.
Our approach leverages the same factors that make current cumbersome
paper-based solutions successful: it can be taught fairly rapidly,
no need for complex motor movements, portable to many settings, and
the listener/receiver of the message is not required to be familiar
with the system. The target user group is young or severe autistic
children who need either a first learning device or who will never
go beyond using image-based communication.
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HealthNet: networking the WESTs (Wireless Embedded medical SysTems)
Mario Gerla and Majid Sarrafzadeh, UCLA Department of Computer
Science, U.S.
This project is a multidisciplinary approach for the detection,
monitoring and treatment of diseases using a Body Sensor Network
(BSN). The BSN, made up of both non-invasive and “in-vivo”
sensors, make it possible to monitor physiological activities
occurring inside of the body and simultaneously probe the outside
environment for harmful chemicals, dangerous radiation levels, and a
more general score of other hostile events. There are a broad range
of biomedical and telemedicine applications for this type of system
ranging from disaster recovery to real-time patient
monitoring/treatment.
There are two components to be developed in conjunction with this
project: Wireless Embedded medical SysTems (WESTs) and HealthNet.
WESTs will investigate:
- Efficient monitoring of physiological occurrences through
noninvasive and in-vivo biomedical sensors exploiting the use of BSN
interworkings for peer-to-peer (P2P) networking.
- Wireless propagation of sensor signals from within the body
(including, but not limited to movement, physical patient
characteristics such as body mass, involuntary bodily processes,
relative distance, location, and direction)
HealthNet will investigate:
- Communications with current
infrastructures (cellular network) and with ad hoc peers
- Delay tolerant operations and communication
- Security/privacy of data on lightweight medical monitoring
devices/systems
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OsteoConduct: Musculo-Skeletal Conduction for Secure Data
Communication
Michael Liebschner and Lin Zhong, Rice University, U.S.
This project uses bone conduction as a new interface between
computers and human users. The development of this technology will
have three broad implications: 1) new user interfaces for hands-free
operation of wearable or mobile computing devices, 2) highly secure
data transmission and user authentication for reliable and protected
access to confidential computing resources and 3) new mechanisms for
wearable and mobile computing devices to provide diagnostic
services.
Communication is established within the skeletal system of the body
itself enabling straightforward communications between implantable
devices and devices attached to other parts of the body surface. The
power requirements change if the wearer consciously recognizes the
functionality of the system or he/she is unconscious of it, as would
be for a constant health monitoring system of critically ill
patients. Signal transmission depends on the patient’s anatomy and
physiology enabling user authentication. The same technology
enables a diagnostic reading of these parts of the human body.
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Enabling Wireless Sensor Devices for Health Care Networks
Leo Selavo and John A. Stankovic, University of Virginia, U.S.
The objective is to meet the two challenges raised by integrating
wireless sensor networks (WSNs) in health care: (1) delivery of
collected data from patients to the monitoring personnel and (2)
unobtrusiveness. Cell phones are a natural choice since they do not
impair patient mobility.
The most convenient wireless interface between the cell phones and
sensors is Bluetooth. However, a Bluetooth connection allows for a
limited number of nodes in the local network (up to seven), and uses
a rather complex communications protocol resulting in higher
software overhead and power consumption. Many networked sensor
systems use the low power IEEE 802.11.4 instead of Bluetooth. The
goal of this project is to create a wireless gateway called
“BlueGate” that enables communications between the two types of
devices.
Sensors monitoring patients must be small, lightweight, have long
lifetime with no need of changing or charging the batteries, and low
cost to be available to large population. Current sensory devices
appear to have a large form factor and are unattractive to patients
or medical personnel. We propose developing a new and compact body
network sensor device that can be attached to patient's clothing or
worn as a wrist watch. The device would collect vital data and
report it to the rest of the WSN. The scope of the work includes low
power hardware design from COTS components as well as power
efficient communications protocols for such mobile nodes that may
roam in and out of the reach of the network.
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Measuring circadian activity deviation for activity pattern
recognition and smart power consumption in Wireless Sensor Networks
Gilles Virone and John A. Stankovic, University of Virginia, U.S.
One objective of this project is to identify activity pattern within
the home using a Wireless Sensor Network, some motion sensors, and a
pattern mining software application for smart healthcare. The study
would be based on the recent concept of Circadian Activity Rhythms
(CAR), combined with advanced data mining techniques. This project
will extend the CAR model by adding a pre-processing K-Means
Clustering method. This should enable the differentiation of the
days of the week into similar “behavioral days” instead of relying
on the assumption that the only significant differences occur
between weekends and weekdays. An alarm classifier will also be
added to limit the number of false detections. The result will be a
refinement of patient monitoring and ultimately improvements in
clinical information and medical diagnoses.
An additional goal is to adapt the power consumption of motes
depending on the behavior of the resident learned through the new
extended CAR patterns. A distributed power management (pm) scheme
will be established, implemented into the WSN, and tested on the
Alarm-Net experimental platform. This will result in a new
power-efficient way to apprehend energy consumption and increase
motes life time by mapping the behavior of the motes to the behavior
of the resident in WSNs applied to the medical domain.
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Cortically-Coupled Computing
Rajesh Rao, University of Washington, U,S.
Cortically-coupled computing is the idea of harnessing the brain’s
information processing capabilities for solving difficult tasks and
hard computational problems. There are a number of applications for
such a technology – direct brain control of robotic devices (e.g.,
prosthetics), brain-based cursor control for communication (e.g.,
for the paralyzed), high-throughput image search for triage
purposes, monitoring cognitive load for user interface research,
etc.
Much of the past research in cortically-coupled computing has relied
on measuring brain activity using electroencephalography (EEG),
which involves recording electrical signals from different locations
on the scalp. EEG is popular because it is a non-invasive and
relatively inexpensive method for recording brain signals. However,
EEG signals are also notoriously noisy and their relation to neural
activity is still poorly understood.
We propose to investigate the relationship between brain surface
recordings (electrocorticography or ECoG) and scalp recordings
(EEG). We seek to build on past research and shed new light on the
relationship between EEG and ECoG by collecting and analyzing
simultaneously recorded EEG and ECoG data.
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