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Adaptive Deep Learning for a Vision-based Fall Detection

Published: 26 June 2018 Publication History

Abstract

Fall is one of the main causes of severe accidents or even death especially for the elderly. Thus, it is imminent to prevent falls before they occur. In this paper, a vision-based system is adopted for fall detection exploiting novel self-adaptable deep machine learning strategies. The deep network is exploited to distinguished humans (foreground) from the background. Adaptation is necessary to tackle dynamic changes in the visual conditions (shadows, illumination, background changes) which are very often for a real-life environment. For the adaptable we are based on a decision mechanism that enable network retraining whenever the visual conditions are not proper for foreground/background separation. Then, a constraint minimization algorithm is activated to optimally estimate new network weights so that i) data from the current visual environment are trusted as much as possible while ii) a minimal degradation of the already existing network knowledge is accomplished. For the activation of the algorithm a set of new labeled data from the current environment is selected by constraining iterative motion information with a human face/body modeler. Experimental results and comparisons with non-adaptable deep network schemes or shallow non-linear classifier indicate the superior performance of the algorithm than other approaches.

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    cover image ACM Other conferences
    PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
    June 2018
    591 pages
    ISBN:9781450363907
    DOI:10.1145/3197768
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 June 2018

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    Author Tags

    1. Deep learning
    2. dynamic adaptation
    3. vision-based fall detection

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    • (2024)Human Activity Recognition in Enhancing Healthcare for Aging Populations: Challenges, Innovations, and Future Directions2024 1st International Conference on Innovative and Intelligent Information Technologies (IC3IT)10.1109/IC3IT63743.2024.10869423(1-6)Online publication date: 3-Dec-2024
    • (2022)Camera based Activity Recognition for Assisted Living Applications2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA)10.1109/eSmarTA56775.2022.9935136(1-8)Online publication date: 25-Oct-2022
    • (2022)Applying deep learning technology for automatic fall detection using mobile sensorsBiomedical Signal Processing and Control10.1016/j.bspc.2021.10335572(103355)Online publication date: Feb-2022
    • (2021)Privacy preserving getup detectionProceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference10.1145/3453892.3453905(234-243)Online publication date: 29-Jun-2021
    • (2021)Wearable System for Personalized and Privacy-preserving Egocentric Visual Context Detection using On-device Deep LearningAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461684(35-40)Online publication date: 21-Jun-2021
    • (2021)A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directionsKnowledge-Based Systems10.1016/j.knosys.2021.106970223(106970)Online publication date: Jul-2021
    • (2020)Hardware/Software Co-Design of Fractal Features Based Fall Detection SystemSensors10.3390/s2008232220:8(2322)Online publication date: 18-Apr-2020
    • (2020)Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector QuantizationComputational Intelligence and Neuroscience10.1155/2020/88218682020Online publication date: 1-Jan-2020
    • (2020)Fall Detection Based on RetinaNet and MobileNet Convolutional Neural Networks2020 15th International Conference on Computer Engineering and Systems (ICCES)10.1109/ICCES51560.2020.9334570(1-7)Online publication date: 15-Dec-2020
    • (2020)Elderly fall detection based on multi-stream deep convolutional networksMultimedia Tools and Applications10.1007/s11042-020-08812-xOnline publication date: 25-Mar-2020
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