8,172 research outputs found
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
We propose a Bayesian model for extracting sleep patterns from smartphone
events. Our method is able to identify individuals' daily sleep periods and
their evolution over time, and provides an estimation of the probability of
sleep and wake transitions. The model is fitted to more than 400 participants
from two different datasets, and we verify the results against ground truth
from dedicated armband sleep trackers. We show that the model is able to
produce reliable sleep estimates with an accuracy of 0.89, both at the
individual and at the collective level. Moreover the Bayesian model is able to
quantify uncertainty and encode prior knowledge about sleep patterns. Compared
with existing smartphone-based systems, our method requires only screen on/off
events, and is therefore much less intrusive in terms of privacy and more
battery-efficient
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
The Internet of Things (IoT) is a dynamic global information network
consisting of internet-connected objects, such as Radio-frequency
identification (RFIDs), sensors, actuators, as well as other instruments and
smart appliances that are becoming an integral component of the future
internet. Over the last decade, we have seen a large number of the IoT
solutions developed by start-ups, small and medium enterprises, large
corporations, academic research institutes (such as universities), and private
and public research organisations making their way into the market. In this
paper, we survey over one hundred IoT smart solutions in the marketplace and
examine them closely in order to identify the technologies used,
functionalities, and applications. More importantly, we identify the trends,
opportunities and open challenges in the industry-based the IoT solutions.
Based on the application domain, we classify and discuss these solutions under
five different categories: smart wearable, smart home, smart, city, smart
environment, and smart enterprise. This survey is intended to serve as a
guideline and conceptual framework for future research in the IoT and to
motivate and inspire further developments. It also provides a systematic
exploration of existing research and suggests a number of potentially
significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
A double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system
This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.Postprint (published version
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