15,204 research outputs found
Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation
This paper presents a novel autonomous quality metric to quantify the
rehabilitations progress of subjects with knee/hip operations. The presented
method supports digital analysis of human gait patterns using smartphones. The
algorithm related to the autonomous metric utilizes calibrated acceleration,
gyroscope and magnetometer signals from seven Inertial Measurement Unit
attached on the lower body in order to classify and generate the grading system
values. The developed Android application connects the seven Inertial
Measurement Units via Bluetooth and performs the data acquisition and
processing in real-time. In total nine features per acceleration direction and
lower body joint angle are calculated and extracted in real-time to achieve a
fast feedback to the user. We compare the classification accuracy and
quantification capabilities of Linear Discriminant Analysis, Principal
Component Analysis and Naive Bayes algorithms. The presented system is able to
classify patients and control subjects with an accuracy of up to 100\%. The
outcomes can be saved on the device or transmitted to treating physicians for
later control of the subject's improvements and the efficiency of physiotherapy
treatments in motor rehabilitation. The proposed autonomous quality metric
solution bears great potential to be used and deployed to support digital
healthcare and therapy.Comment: 5 Page
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
Synchronous Relaying Of Sensor Data
In this paper we have put forth a novel methodology to relay data obtained by
inbuilt sensors of smart phones in real time to remote database followed by
fetching of this data . Smart phones are becoming very common and they are
laced with a number of sensors that can not only be used in native applications
but can also be sent to external nodes to be used by third parties for
application and service development
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