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Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network: Poster Abstract

Authors:

Seokhyun Hwang,

DaeHan Ahn,

Homin Park,

Taejoon ParkAuthors Info & Claims

IoTDI '17: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

Pages 343 - 344

https://doi.org/10.1145/3054977.3057314

Published: 18 April 2017 Publication History

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Abstract

We present a deep-learning-based approach to maximize the accuracy and reliability of vision-based fall detection and alert systems. The proposed approach utilizes a 3D convolutional neural network (3D-CNN) to analyze the continuous motion data obtained from depth cameras and exploits a data augmentation method to do away with overfitting. Our preliminary evaluation results demonstrate that it achieves the classification accuracy of up to 96.9%.

References

[1]

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Xin Ma, Haibo Wang, Bingxia Xue, Mingang Zhou, Bing Ji, and Yibin Li. 2014. Depth-based human fall detection via shape features and improved extreme learning machine. IEEE journal of biomedical and health informatics 18, 6 (2014), 1915--1922.

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Yoosuf Nizam, Mohd Norzali Haji Mohd, and M. Mahadi Abdul Jamil. 2016. A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques. International Journal of Integrated Engineering 8, 1 (January 2016), 35--43.

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Hee Jung Yoon, Ho-Kyeong Ra, Taejoon Park, Sam Chung, and Sang Hyuk Son. 2015. FADES: Behavioral detection of falls using body shapes from 3D joint data. Journal of Ambient Intelligence and Smart Environments 7, 6 (2015), 861--877.

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Egawa RMiah AHirooka KTomioka YShin J(2023)Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention NetworkElectronics10.3390/electronics1215323412:15(3234)Online publication date: 26-Jul-2023
https://doi.org/10.3390/electronics12153234
Cheng YChen GZhou QLiu CCai C(2023)CG-Net: A Novel End-to-end Framework for Fall Detection from Videos2023 8th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS57501.2023.10151063(984-988)Online publication date: 21-Apr-2023
https://doi.org/10.1109/ICCCS57501.2023.10151063
Vishwakarma DJain K(2022)Three‐dimensional human activity recognition by forming a movement polygon using posture skeletal data from depth sensorETRI Journal10.4218/etrij.2020-010144:2(286-299)Online publication date: 25-Jan-2022
https://doi.org/10.4218/etrij.2020-0101
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Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network: Poster Abstract
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

IoTDI '17: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

April 2017

353 pages

ISBN:9781450349666

DOI:10.1145/3054977

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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Association for Computing Machinery

New York, NY, United States

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Published: 18 April 2017

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Ministry of Science, ICT and Future Planning

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IoTDI '17

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SIGBED

IoTDI '17: International Conference on Internet-of-Things Design and Implementation

April 18 - 21, 2017

PA, Pittsburgh, USA

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Egawa RMiah AHirooka KTomioka YShin J(2023)Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention NetworkElectronics10.3390/electronics1215323412:15(3234)Online publication date: 26-Jul-2023
https://doi.org/10.3390/electronics12153234
Cheng YChen GZhou QLiu CCai C(2023)CG-Net: A Novel End-to-end Framework for Fall Detection from Videos2023 8th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS57501.2023.10151063(984-988)Online publication date: 21-Apr-2023
https://doi.org/10.1109/ICCCS57501.2023.10151063
Vishwakarma DJain K(2022)Three‐dimensional human activity recognition by forming a movement polygon using posture skeletal data from depth sensorETRI Journal10.4218/etrij.2020-010144:2(286-299)Online publication date: 25-Jan-2022
https://doi.org/10.4218/etrij.2020-0101
Katsamenis IBakalos NKarolou EDoulamis ADoulamis N(2022)Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime EnvironmentsTechnologies10.3390/technologies1002004710:2(47)Online publication date: 29-Mar-2022
https://doi.org/10.3390/technologies10020047
Pȩkala BMroczek TGil DKepski M(2022)Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection SystemSensors10.3390/s2204160222:4(1602)Online publication date: 18-Feb-2022
https://doi.org/10.3390/s22041602
Shah STahir ALe Kernec JZoha AFioranelli F(2022)Data portability for activities of daily living and fall detection in different environments using radar micro-dopplerNeural Computing and Applications10.1007/s00521-022-06886-234:10(7933-7953)Online publication date: 19-Jan-2022
https://doi.org/10.1007/s00521-022-06886-2
Chen ZWang YYang W(2022)Video Based Fall Detection Using Human PosesBig Data10.1007/978-981-16-9709-8_19(283-296)Online publication date: 15-Jan-2022
https://doi.org/10.1007/978-981-16-9709-8_19
Duc QPhung TNguyen MNguyen BNguyen T(2022)Self-knowledge Distillation: An Efficient Approach for Falling DetectionArtificial Intelligence in Data and Big Data Processing10.1007/978-3-030-97610-1_29(369-380)Online publication date: 19-May-2022
https://doi.org/10.1007/978-3-030-97610-1_29
Keskes ONoumeir R(2021)Vision-Based Fall Detection Using ST-GCNIEEE Access10.1109/ACCESS.2021.30582199(28224-28236)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3058219
Seredin OKopylov ASurkov E(2020)The study of skeleton description reduction in the human fall-detection taskComputer Optics10.18287/2412-6179-CO-75344:6(951-958)Online publication date: Dec-2020
https://doi.org/10.18287/2412-6179-CO-753
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