12,553 research outputs found
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
Predicting customer's gender and age depending on mobile phone data
In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain
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
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