Cited By
View all- Correia SRoque PMatos-Carvalho J(2024)LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection SensorsSymmetry10.3390/sym1610129616:10(1296)Online publication date: 2-Oct-2024
Quantization with Gate Disclosure for Embedded Artificial Intelligence Applied to Fall Detection
Pages 84 - 87
Abstract
Fall detection in the elderly population is a typical application of pattern recognition, where machine learning algorithms have shown good performance results in the scientific literature. Nevertheless, the usual large dimension of the networks proposes a challenge for embedded implementations that could be used on wearable or wireless sensor networks. The most common implementation relies on edge or cloud computing of the algorithms, which triggers potential privacy issues and poses a challenge in terms of a large amount of data that need to be transferred through a communication channel. The current work proposes a new methodology for performing network quantization. The extensive simulation results demonstrate the effectiveness and feasibility of the employed methodology for embedded implementation of the LSTM for the fall detection problem on wearable platforms.
References
[1]
Yogesh Angal and Arti Jagtap. 2016. Fall detection system for older adults. 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT) (2016). https://doi.org/10.1109/icaecct.2016.7942595
[2]
Serkan Balli, Ensar Arif Sağbaş, and Musa Peker. 2019. Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm. Measurement and Control 52, 1-2 (2019), 37–45.
[3]
Juan Carlos Briede-Westermeyer, Bélgica Pacheco-Blanco, Marianela Luzardo-Briceño, and Cristhian Pérez-Villalobos. 2020. Mobile Phone Use by the Elderly: Relationship between Usability, Social Activity, and the Environment. Sustainability 12, 7 (2020), 2690. https://doi.org/10.3390/su12072690
[4]
Luis N. Valcourt Colón, Yueng DeLaHoz, and Miguel Labrador. 2014. Human fall detection with smartphones. In 2014 IEEE Latin-America Conference on Communications (LATINCOM). 1–7. https://doi.org/10.1109/LATINCOM.2014.7041879
[5]
Sérgio D. Correia, Slavisa Tomic, and Marko Beko. 2021. A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization. Journal of Sensor and Actuator Networks 10, 2 (2021). https://doi.org/10.3390/jsan10020029
[6]
Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Hang Su, and Jun Zhu. 2019. Stochastic quantization for learning accurate low-bit deep neural networks. International Journal of Computer Vision 127, 11 (2019), 1629–1642.
[7]
Jesús Gutiérrez, Víctor Rodríguez, and Sergio Martin. 2021. Comprehensive Review of Vision-Based Fall Detection Systems. Sensors 21, 3 (2021), 947. https://doi.org/10.3390/s21030947
[8]
Qingchang Han, Yongmin Hu, Fengwei Yu, Hailong Yang, Bing Liu, Peng Hu, Ruihao Gong, Yanfei Wang, Rui Wang, Zhongzhi Luan, 2020. Extremely low-bit convolution optimization for quantized neural network on modern computer architectures. In 49th International Conference on Parallel Processing-ICPP. 1–12.
[9]
Shang-Lin Hsieh, Ming Hsiung Su, Lu Feng Liu, and Wey-Wen Jiang. 2012. A Finite State Machine-Based Fall Detection Mechanism on Smartphones. In 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing. 735–739. https://doi.org/10.1109/UIC-ATC.2012.153
[10]
Md Milon Islam, Nieb Neom, Md Samir Imtiaz, Sheikh Nooruddin, Md Repon Islam, and Md Rabiul Islam. 2019. A Review on Fall Detection Systems Using Data from Smartphone Sensors.Ingénierie des systèmes d Inf. 24, 6 (2019), 569–576.
[11]
Md Milon Islam, Omar Tayan, Md Repon Islam, Md Saiful Islam, Sheikh Nooruddin, Muhammad Nomani Kabir, and Md Rabiul Islam. 2020. Deep learning based systems developed for fall detection: a review. IEEE Access 8 (2020), 166117–166137.
[12]
Md. Milon Islam, Omar Tayan, Md. Repon Islam, Md. Saiful Islam, Sheikh Nooruddin, Muhammad Nomani Kabir, and Md. Rabiul Islam. 2020. Deep Learning Based Systems Developed for Fall Detection: A Review. IEEE Access 8 (2020), 166117–166137. https://doi.org/10.1109/ACCESS.2020.3021943
[13]
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi. 2019. Learning to quantize deep networks by optimizing quantization intervals with task loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4350–4359.
[14]
Chien-Liang Liu, Chia-Hoang Lee, and Ping-Min Lin. 2010. A fall detection system using k-nearest neighbor classifier. Expert systems with applications 37, 10 (2010), 7174–7181.
[15]
Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P Xing, and Zhiqiang Shen. 2022. Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4942–4952.
[16]
João P. Matos-Carvalho, Sérgio D. Correia, and Slavisa Tomic. 2023. Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors. In 2023 6th Conference on Cloud and Internet of Things (CIoT). 112–118. https://doi.org/10.1109/CIoT57267.2023.10084906
[17]
Gulgun Mistikoglu, Ibrahim Halil Gerek, Ercan Erdis, PE Mumtaz Usmen, Hulya Cakan, and Emrah Esref Kazan. 2015. Decision tree analysis of construction fall accidents involving roofers. Expert Systems with Applications 42, 4 (2015), 2256–2263.
[18]
Jiraporn Sri-on, Gregory P. Tirrell, Jonathan F. Bean, Lewis A. Lipsitz, and Shan W. Liu. 2017. Revisit, subsequent hospitalization, recurrent fall, and death within 6 months after a fall among elderly emergency department patients. Annals of Emergency Medicine 70, 4 (2017). https://doi.org/10.1016/j.annemergmed.2017.05.023
[19]
Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Viji Srinivasan, and Kailash Gopalakrishnan. 2020. Ultra-low precision 4-bit training of deep neural networks. Advances in Neural Information Processing Systems 33 (2020), 1796–1807.
[20]
Nicolas Thome, Serge Miguet, and SÉbastien Ambellouis. 2008. A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach. IEEE Transactions on Circuits and Systems for Video Technology 18, 11 (2008), 1522–1532. https://doi.org/10.1109/TCSVT.2008.2005606
[21]
Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, and Kailash Gopalakrishnan. 2018. Training deep neural networks with 8-bit floating point numbers. Advances in neural information processing systems 31 (2018).
[22]
World Health Organization. 2015. World Report on Ageing and Health. World Health Organization, Geneva, Switzerland.
[23]
Jiwei Yang, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, and Xian-sheng Hua. 2019. Quantization networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7308–7316.
[24]
Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing Xu, Chao Xu, Dacheng Tao, and Chang Xu. 2020. Searching for low-bit weights in quantized neural networks. Advances in neural information processing systems 33 (2020), 4091–4102.
[25]
William R. Young and A. Mark Williams. 2015. How fear of falling can increase fall-risk in older adults: Applying psychological theory to practical observations. Gait Posture 41, 1 (2015), 7–12. https://doi.org/10.1016/j.gaitpost.2014.09.006
[26]
Tong Zhang, Jue Wang, Liang Xu, and Ping Liu. 2006. Fall detection by wearable sensor and one-class SVM algorithm. In Intelligent computing in signal processing and pattern recognition. Springer, 858–863.
[27]
Wenyu Zhao, Teli Ma, Xuan Gong, Baochang Zhang, and David Doermann. 2021. A Review of Recent Advances of Binary Neural Networks for Edge Computing. IEEE Journal on Miniaturization for Air and Space Systems 2, 1 (2021), 25–35. https://doi.org/10.1109/JMASS.2020.3034205
Index terms have been assigned to the content through auto-classification.
Recommendations
Robust Blind Image Watermarking Algorithm Based On Singular Value Quantization
ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and ServiceIn this paper, a new robust blind watermarking algorithm based on singular value quantization is proposed to solve the issues of image watermarking robustness, invisibility and blind extraction. In order to improve the security of watermark information, ...
Design of a Compressive Sensing Based Fall detection System for Elderly Using WSN
Recent researches have pointed out that one third persons are aged 65 and above requires special health care. As the number of elderly person is increasing, home monitoring for healthcare applications is playing a vital role in our daily life. Falls are ...
Effect of Quantization on the Robustness of DCT Digital Watermarking Techniques
ICIMT '09: Proceedings of the 2009 International Conference on Information and Multimedia TechnologyDigital watermarking addresses the growing concerns of theft and tampering of digital media through the use of advanced signal processing strategies to embed copyright and authentication information within media content. This paper aims to implement two ...
Comments
Information & Contributors
Information
Published In
September 2024
481 pages
ISBN:9798400710940
DOI:10.1145/3677525
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 September 2024
Check for updates
Author Tags
Qualifiers
- Work in progress
- Research
- Refereed limited
Conference
GoodIT '24
Sponsor:
GoodIT '24: International Conference on Information Technology for Social Good
September 4 - 6, 2024
Bremen, Germany
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 33Total Downloads
- Downloads (Last 12 months)33
- Downloads (Last 6 weeks)11
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
Cited By
View all- Correia SRoque PMatos-Carvalho J(2024)LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection SensorsSymmetry10.3390/sym1610129616:10(1296)Online publication date: 2-Oct-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format