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Applying machine learning to predict future adherence to physical activity programs.
BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011
PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data
Audio-visual learning seeks to enhance the computer's multi-modal perception
leveraging the correlation between the auditory and visual modalities. Despite
their many useful downstream tasks, such as video retrieval, AR/VR, and
accessibility, the performance and adoption of existing audio-visual models
have been impeded by the availability of high-quality datasets. Annotating
audio-visual datasets is laborious, expensive, and time-consuming. To address
this challenge, we designed and developed an efficient audio-visual annotation
tool called Peanut. Peanut's human-AI collaborative pipeline separates the
multi-modal task into two single-modal tasks, and utilizes state-of-the-art
object detection and sound-tagging models to reduce the annotators' effort to
process each frame and the number of manually-annotated frames needed. A
within-subject user study with 20 participants found that Peanut can
significantly accelerate the audio-visual data annotation process while
maintaining high annotation accuracy.Comment: 18 pages, published in UIST'2
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
On the Feature Discovery for App Usage Prediction in Smartphones
With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape
Characterization of the Lightning Safety Education Programs in the World as a First Step for the Creation of a Lightning Safety Policy in Colombia
En este proyecto de investigación-innovación se presentan los resultados de un estudio sobre los programas de educación en prevención contra rayos que han sido implementados en el mundo. La importancia del estudio de estos programas se fundamenta en las múltiples lesiones asociadas a las descargas eléctricas atmosféricas que se han observado en Colombia durante los últimos años. Una investigación primaria es llevada a cabo para determinar cuáles podrían ser los medios de difusión más pertinentes para transmitir el mensaje de la prevención contra rayos a la población rural, con el fin de tener los elementos necesarios para plantear una metodología educativa que pueda ser el primer paso para la creación de una política pública en prevención y protección contra rayos en Colombia.This research-innovation project presents the results of a study on the education programs on lightning safety that have been implemented in the world. The importance of the study of these programs is based on the multiple injuries associated with atmospheric discharges that have been observed in Colombia during the last years. A primary research is conducted to determine which could be the most pertinent diffusion means to spread the lightning safety message among the rural population, in order to have the necessary elements to propose an educational methodology that may be the first step towards the creation of a lightning safety public policy in Colombia
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