research-article

Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier

Authors:

Yuwei ZhuAuthors Info & Claims

ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

Pages 83 - 86

https://doi.org/10.1145/3232059.3232065

Published: 16 May 2018 Publication History

Get Access

Abstract

For the purpose of improving the medical care which aims at the elderly and the chronic patients who are prone to falls, this paper makes use of Standard Binary Particle Swarm Optimization(SBPSO) to search for the combination of best feature subset and parameters (C, g), which can be used to train the SVM(Support Vector Machine). Experiments results show that the proposed method can get higher accuracy (about 99%) compared with non-optimized SVM, k-NN (k Nearest Neighbors) and threshold-based method when dealing with the classification of ADL (Activities in Daily Life) and abnormal falls.

References

[1]

P. Hawrank, A clinical possibility: preventing health problems after age of 65. Journal of gerontological nursing, 1991. pp. 20-25.

Google Scholar

[2]

X. F. Li, Z. G. Bai, Analysis of the current situation of telemedicine in China, Chines Journal of Evidence-Based Medicine, 2013. pp. 1194-1199.

Google Scholar

[3]

Nyan. Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Medical Engineering & Physics, 2006. pp. 842-849.

Google Scholar

[4]

A. M. Khan, Y. K. Lee, S. Y. Lee, T. S. Kim, A tri-axial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. Information Technology in Biomedicine, IEEE Transactions on, 2010. pp. 1166-1172.

Digital Library

Google Scholar

[5]

M. Jiang, Z. L. Wang, X. B. Liu, H. Y. Zhao, Y. H. Hu. Research on Human Daily Activity recognition Method Based on BSN and CHMMs. Journal of Dalian University of Technology. 2013. vol.53. pp. 121-126.

Google Scholar

[6]

Y. Nantakrit, S. Teppakorn. The development of Artificial Neural Networks (ANN) for Falls Detection. the 3rd International Conference on Control, Automation and Robotics, 2017.

Google Scholar

[7]

L. Q. Chen. Human Action Recognition Based on EEMD and Fuzzy LS-SVM. Dalian University of Technology. 2014.

Google Scholar

[8]

Y. K. Ning. Design of MEMS Inertial Data Collection Platform and its application in fall detection. Guilin University of Electronics and Technology. 2013.

Google Scholar

[9]

J. Anice, N. T. Marjan, J. R. Mohammad. Accurate Fall Detection Using 3-Axis Accelerometer Sensor And MLF Algorithm. 2017, the 3rd International Conference on Pattern Recognition and Image Analysis.

Google Scholar

[10]

Y. Liu. Fall Detector Design Based on Pattern Recognition. College of Communication Engineering of Chongqing, China, 2014.

Google Scholar

[11]

H. N. Young, G. L. Jong, D. E. Kim, D. S. Kwon. User-Adaptive Fall Detection for Patients Using Wristband. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Google Scholar

[12]

X. Liu. Feature Selection Methods Based on Particle Swarm Optimization. Nanjing University, China, 2015.

Google Scholar

[13]

Cervante, L. Xue, B. Zhang, M. Shang, Binary Particle Swarm Optimisation for Feature Selection: A Filter based approach, in: IEEE Congress on Evolutionary Computation, pp. 1038-1042.

Google Scholar

Cited By

View all

SÖZER A(2022)Denetimsiz Anomali Tespiti Yaklaşımı ile Düşme AlgılamaBitlis Eren Üniversitesi Fen Bilimleri Dergisi10.17798/bitlisfen.99776011:1(88-98)Online publication date: 24-Mar-2022
https://doi.org/10.17798/bitlisfen.997760
Saha ARajak SSaha JChowdhury C(2022)A Survey of Machine Learning and Meta-heuristics Approaches for Sensor-based Human Activity Recognition SystemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-03870-515:1(29-56)Online publication date: 21-May-2022
https://doi.org/10.1007/s12652-022-03870-5
Nho YLim JKwon D(2020)Cluster-Analysis-based User-Adaptive Fall Detection using Fusion of Heart Rate Sensor and Accelerometer in a Wearable DeviceIEEE Access10.1109/ACCESS.2020.2969453(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2969453
Show More Cited By

Index Terms

Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier
1. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Bio-embedded electronics

Recommendations

Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic

Graphical abstractDisplay Omitted HighlightsFall detection and diagnostic with combined classifier system.Wearable Shimmer embedded device implementation.Event consistency with majority voting and outsourced supervisor.Significant system performance ...
A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a ...
Adaboost with SVM-based classifier for the classification of brain motor imagery tasks
UAHCI'11: Proceedings of the 6th international conference on Universal access in human-computer interaction: users diversity - Volume Part II

The Adaboost with SVM-based component classifier is generally considered to break the Boosting principle for the difficulty in training of SVM and have imbalance between the diversity and accuracy over basic SVM classifiers. The Adaboost classifier in ...

Comments

Information & Contributors

Information

Published In

ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 2018

93 pages

ISBN:9781450363662

DOI:10.1145/3232059

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]

In-Cooperation

Universidade Nova de Lisboa

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICBBT '18

ICBBT '18: 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 16 - 18, 2018

Amsterdam, Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
62
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

SÖZER A(2022)Denetimsiz Anomali Tespiti Yaklaşımı ile Düşme AlgılamaBitlis Eren Üniversitesi Fen Bilimleri Dergisi10.17798/bitlisfen.99776011:1(88-98)Online publication date: 24-Mar-2022
https://doi.org/10.17798/bitlisfen.997760
Saha ARajak SSaha JChowdhury C(2022)A Survey of Machine Learning and Meta-heuristics Approaches for Sensor-based Human Activity Recognition SystemsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-022-03870-515:1(29-56)Online publication date: 21-May-2022
https://doi.org/10.1007/s12652-022-03870-5
Nho YLim JKwon D(2020)Cluster-Analysis-based User-Adaptive Fall Detection using Fusion of Heart Rate Sensor and Accelerometer in a Wearable DeviceIEEE Access10.1109/ACCESS.2020.2969453(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2969453
Jahanjoo ANaderan MRashti M(2020)Detection and multi-class classification of falling in elderly people by deep belief network algorithmsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-01690-zOnline publication date: 8-Jan-2020
https://doi.org/10.1007/s12652-020-01690-z

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic

A smartphone-based fall detection system

Adaboost with SVM-based classifier for the classification of brain motor imagery tasks

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations