5,440 research outputs found

    Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

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    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

    Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

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    Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft

    dWatch: a Personal Wrist Watch for Smart Environments

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    Intelligent environments, such as smart homes or domotic systems, have the potential to support people in many of their ordinary activities, by allowing complex control strategies for managing various capabilities of a house or a building: lights, doors, temperature, power and energy, music, etc. Such environments, typically, provide these control strategies by means of computers, touch screen panels, mobile phones, tablets, or In-House Displays. An unobtrusive and typically wearable device, like a bracelet or a wrist watch, that lets users perform various operations in their homes and to receive notifications from the environment, could strenghten the interaction with such systems, in particular for those people not accustomed to computer systems (e.g., elderly) or in contexts where they are not in front of a screen. Moreover, such wearable devices reduce the technological gap introduced in the environment by home automation systems, thus permitting a higher level of acceptance in the daily activities and improving the interaction between the environment and its inhabitants. In this paper, we introduce the dWatch, an off-the-shelf personal wearable notification and control device, integrated in an intelligent platform for domotic systems, designed to optimize the way people use the environment, and built as a wrist watch so that it is easily accessible, worn by people on a regular basis and unobtrusiv

    Human activity recognition making use of long short-term memory techniques

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    The optimisation and validation of a classifiers performance when applied to real world problems is not always effectively shown. In much of the literature describing the application of artificial neural network architectures to Human Activity Recognition (HAR) problems, postural transitions are grouped together and treated as a singular class. This paper proposes, investigates and validates the development of an optimised artificial neural network based on Long-Short Term Memory techniques (LSTM), with repeated cross validation used to validate the performance of the classifier. The results of the optimised LSTM classifier are comparable or better to that of previous research making use of the same dataset, achieving 95% accuracy under repeated 10-fold cross validation using grouped postural transitions. The work in this paper also achieves 94% accuracy under repeated 10-fold cross validation whilst treating each common postural transition as a separate class (and thus providing more context to each activity)

    Detecting changes of transportation-mode by using classification data

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