3,253 research outputs found
Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques
Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject
ZOE: A cloud-less dialog-enabled continuous sensing wearable exploiting heterogeneous computation
The wearable revolution, as a mass-market phenomenon, has finally
arrived. As a result, the question of how wearables should evolve
over the next 5 to 10 years is assuming an increasing level of societal
and commercial importance. A range of open design and
system questions are emerging, for instance: How can wearables
shift from being largely health and fitness focused to tracking a
wider range of life events? What will become the dominant methods
through which users interact with wearables and consume the
data collected? Are wearables destined to be cloud and/or smartphone
dependent for their operation?
Towards building the critical mass of understanding and experience
necessary to tackle such questions, we have designed and
implemented ZOE – a match-box sized (49g) collar- or lapel-worn
sensor that pushes the boundary of wearables in an important set of
new directions. First, ZOE aims to perform multiple deep sensor
inferences that span key aspects of everyday life (viz. personal, social
and place information) on continuously sensed data; while also
offering this data not only within conventional analytics but also
through a speech dialog system that is able to answer impromptu
casual questions from users. (Am I more stressed this week than
normal?) Crucially, and unlike other rich-sensing or dialog supporting
wearables, ZOE achieves this without cloud or smartphone
support – this has important side-effects for privacy since all user
information can remain on the device. Second, ZOE incorporates
the latest innovations in system-on-a-chip technology together with
a custom daughter-board to realize a three-tier low-power processor
hierarchy. We pair this hardware design with software techniques
that manage system latency while still allowing ZOE to remain energy
efficient (with a typical lifespan of 30 hours), despite its high
sensing workload, small form-factor, and need to remain responsive to user dialog requests.This work was supported by Microsoft Research through its PhD
Scholarship Program. We would also like to thank the anonymous
reviewers and our shepherd, Jeremy Gummeson, for helping us improve
the paper.This is the author accepted manuscript. The final version is available from ACM at http://dl.acm.org/citation.cfm?doid=2742647.2742672
- …