5,028 research outputs found
Application Heartbeats for Software Performance and Health
Adaptive, or self-aware, computing has been proposed as one method to help application programmers confront the growing complexity of multicore software development. However, existing approaches to adaptive systems are largely ad hoc and often do not manage to incorporate the true performance goals of the applications they are designed to support. This paper presents an enabling technology for adaptive computing systems: Application Heartbeats. The Application Heartbeats framework provides a simple, standard programming interface that applications can use to indicate their performance and system software (and hardware) can use to query an applicationâ s performance. Several experiments demonstrate the simplicity and efficacy of the Application Heartbeat approach. First the PARSEC benchmark suite is instrumented with Application Heartbeats to show the broad applicability of the interface. Then, an adaptive H.264 encoder is developed to show how applications might use Application Heartbeats internally. Next, an external resource scheduler is developed which assigns cores to an application based on its performance as specified with Application Heartbeats. Finally, the adaptive H.264 encoder is used to illustrate how Application Heartbeats can aid fault tolerance
Design of Real Time Heart Rate Monitoring System With ARDUINO UNO R3 Based on Android Application
The heart is an important organ for humans. Heart disease has claimed many lives. It happened because not everyone can do regular heart checks. High cost and time wasted could be the reason. Even a lot of heart disease is detected when a disease was so severe that treatment be late. Though the heart is the organ that affects other organs, in other words, the good performance of the heart as the source of much-needed work of other organs. One of the things that can be done is to check regularly to determine the condition of the heart through a number of heartbeats per minute. By knowing the condition of the heart then anyone can change the pattern of life and maintain food intake better, so that the heart will change to a more healthy condition. Therefore the heart rate monitoring system application to be made. Only by writing gender and age, everyone can use it and do not have to wait for a medical expert. The application can determine the condition of the heart in good condition, weak or bad through the calculation of the number of heartbeats using easy way, so that they can consult with medical experts and of course heart health can be maintained without taking expensive and complicated way, simply by changing lifestyle and exercise regularly. This application runs on Android based smartphones that connected to ARDUINO UNO R3 modul to detect heart rate.
Keywords-heart rate; heart condition; android; arduin
An Internet Heartbeat
Obtaining sound inferences over remote networks via active or passive
measurements is difficult. Active measurement campaigns face challenges of
load, coverage, and visibility. Passive measurements require a privileged
vantage point. Even networks under our own control too often remain poorly
understood and hard to diagnose. As a step toward the democratization of
Internet measurement, we consider the inferential power possible were the
network to include a constant and predictable stream of dedicated lightweight
measurement traffic. We posit an Internet "heartbeat," which nodes periodically
send to random destinations, and show how aggregating heartbeats facilitates
introspection into parts of the network that are today generally obtuse. We
explore the design space of an Internet heartbeat, potential use cases,
incentives, and paths to deployment
Resonating Experiences of Self and Others enabled by a Tangible Somaesthetic Design
Digitalization is penetrating every aspect of everyday life including a
human's heart beating, which can easily be sensed by wearable sensors and
displayed for others to see, feel, and potentially "bodily resonate" with.
Previous work in studying human interactions and interaction designs with
physiological data, such as a heart's pulse rate, have argued that feeding it
back to the users may, for example support users' mindfulness and
self-awareness during various everyday activities and ultimately support their
wellbeing. Inspired by Somaesthetics as a discipline, which focuses on an
appreciation of the living body's role in all our experiences, we designed and
explored mobile tangible heart beat displays, which enable rich forms of bodily
experiencing oneself and others in social proximity. In this paper, we first
report on the design process of tangible heart displays and then present
results of a field study with 30 pairs of participants. Participants were asked
to use the tangible heart displays during watching movies together and report
their experience in three different heart display conditions (i.e., displaying
their own heart beat, their partner's heart beat, and watching a movie without
a heart display). We found, for example that participants reported significant
effects in experiencing sensory immersion when they felt their own heart beats
compared to the condition without any heart beat display, and that feeling
their partner's heart beats resulted in significant effects on social
experience. We refer to resonance theory to discuss the results, highlighting
the potential of how ubiquitous technology could utilize physiological data to
provide resonance in a modern society facing social acceleration.Comment: 18 page
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance
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