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A smartphone-based fall detection system

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TLDR
A smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers, to reduce the problem of false alarms.
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This article is published in Pervasive and Mobile Computing.The article was published on 2012-12-01. It has received 320 citations till now. The article focuses on the topics: Poison control.

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The Internet of Things vision: Key features, applications and open issues

TL;DR: This paper presents the key features and the driver technologies of IoT, and identifies the application scenarios and the correspondent potential applications, and focuses on research challenges and open issues to be faced for the IoT realization in the real world.
Journal ArticleDOI

Challenges, issues and trends in fall detection systems

TL;DR: An extensive literature review of fall detection systems is presented, including comparisons among various kinds of studies, to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations.
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Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges

TL;DR: A comprehensive review on the state-of-the-art research and development in smart home based remote healthcare technologies is presented.
Journal ArticleDOI

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

TL;DR: This article reviews state-of-the-art wearable technologies that can be used for elderly care and discusses a series of considerations and future trends with regard to the construction of “smart clothing” system.
Journal ArticleDOI

An enhanced fall detection system for elderly person monitoring using consumer home networks

TL;DR: An enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks that can achieve a high detection accuracy and a low false positive rate.
References
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Journal ArticleDOI

Activity recognition using cell phone accelerometers

TL;DR: This work describes and evaluates a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing, and has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity.
Book ChapterDOI

TinyOS: An Operating System for Sensor Networks

TL;DR: A qualitative and quantitative evaluation of the TinyOS system is provided, showing that it supports complex, concurrent programs with very low memory requirements and efficient, low-power operation.
Journal ArticleDOI

Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

TL;DR: Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.
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