research-article

A Flexible Approach for Human Activity Recognition Based on Broad Learning System

Authors:

Xuemin HongAuthors Info & Claims

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

Pages 368 - 373

https://doi.org/10.1145/3318299.3318318

Published: 22 February 2019 Publication History

Abstract

Deep Learning (DL) based methods have recently been receiving attention in Human Activity Recognition (HAR) for their strong capability of nonlinear mapping. However, these methods suffer from high time consumption during training process due to enormous network parameters. Moreover, the DL-based scheme is less capable of incremental learning which is important for some online human activity recognition applications. In this paper, the Broad Learning System (BLS) known as a promising alternative to DL-based methods is introduced to the classification of human activities. Both the online and offline BLS-based recognition frameworks are proposed to enhance the system flexibility. Specifically, during the online training stage, the artificial hyperspherical data generation model is incorporated into the incremental BLS, enabling it to update the model to accommodate new incoming data more efficiently. Experiments are made towards the proposed BLS network based upon two public human activity datasets, namely, HART and WISDM. The results demonstrate the advantage of the proposed BLS-based scheme over the classic DL-based approaches in terms of the training speed and prediction accuracy.

References

[1]

Abdallah, Z. S., Gaber, M. M., Srinivasan, B., and Krishnaswamy, S. Adaptive mobile activity recognition system with evolving data streams. Neurocomputing 150 (2015), 304--317.

Digital Library

[2]

Abdallah, Z. S., Gaber, M. M., Srinivasan, B., and Krishnaswamy, S. Activity recognition with evolving data streams: A review. ACM Comput. Surv. 51, 4 (2018), 71.

Digital Library

[3]

Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. A public domain dataset for human activity recognition using smartphones. In Proceedings of European Symposium on Artificial Neural Networks (ESANN) (Apr. 2013).

[4]

Bengio, Y. Deep learning of representations: Looking forward. In Proceedings of International Conference on Statistical Language and Speech Processing (SLSP) (Berlin, Germany, 2013), vol. 7978, Springer-Verlag, pp. 1--37.

Digital Library

[5]

Chen, C. P., and Liu, Z. Broad learning system: a new learning paradigm and system without going deep. In Proceedings 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2017), IEEE, pp. 1271--1276.

[6]

Chen, C. P., and Liu, Z. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29, 1 (July 2018), 10--24.

[7]

Chen, H., Xiong, F., Wu, D., Zheng, L., Peng, A., Hong, X., Tang, B., Lu, H., Shi, H., and Zheng, H. Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017), IEEE, pp. 1160--1165.

[8]

Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. Deep learning, vol. 1. MIT press Cambridge, 2016.

Digital Library

[9]

Hoerl, A. E., and Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42, 1 (2000), 80--86.

Digital Library

[10]

Hu, C., Chen, Y., Hu, L., and Peng, X. A novel random forests based class incremental learning method for activity recognition. Pattern Recognition 78 (2018), 277--290.

Digital Library

[11]

Kwapisz, J. R., Weiss, G. M., and Moore, S. A. Activity recognition using cell phone accelerometers. ACM SIGKDD 12, 2 (Dec. 2010), 74--82.

Digital Library

[12]

Lara, O. D., Labrador, M. A., et al. A survey on human activity recognition using wearable sensors. IEEE Commun. Surveys Tuts. 15, 3 (Nov. 2013), 1192--1209.

[13]

Lu, C.-H., Ho, Y.-C., Chen, Y.-H., and Fu, L.-C. Hybrid user-assisted incremental model adaptation for activity recognition in a dynamic smart-home environment. IEEE Trans. Human-Mach. Syst. 43, 5 (Sept. 2013), 421--436.

[14]

Nair, V., and Hinton, G. E. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (2010), pp. 807--814.

Digital Library

[15]

Nweke, H. F., Teh, Y. W., Al-Garadi, M. A., and Alo, U. R. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst. With Applic. 105 (2018), 233--261.

[16]

Schmidt, W. F., Kraaijveld, M. A., and Duin, R. P. Feedforward neural networks with random weights. In Proceedings of 11th IAPR International Conference on Pattern Recognition Methodology and Systems (Sept. 1992), IEEE, pp. 1--4.

[17]

Tax, D. M., and Duin, R. P. Uniform object generation for optimizing one-class classifiers. Machine Learning Research 2 (Dec. 2001), 155--173.

Digital Library

[18]

Wang, J., Chen, Y., Hao, S., Peng, X., and Hu, L. Deep learning for sensor-based activity recognition: A survey. Pattern Recognit. Lett. (Feb. 2018).

[19]

Wang, Z., Jiang, M., Hu, Y., and Li, H. An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16, 4 (July 2012), 691--699.

Digital Library

[20]

Yang, J., Nguyen, M. N., San, P. P., Li, X., and Krishnaswamy, S. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of International Joint Conferences on Artificial Intelligence (IJCAI), (2015), vol. 15, pp. 3995--4001.

Digital Library

Cited By

Velayudhan DAhmed AHassan TOwais MGour NBennamoun MDamiani EWerghi N(2024)Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage itemsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-024-00182-77:1Online publication date: 24-Dec-2024
https://doi.org/10.1186/s42492-024-00182-7
Ni MLi S(2024)A data-driven LSTMSCBLS model for soft sensor of industrial processMeasurement Science and Technology10.1088/1361-6501/ad5ab835:10(106201)Online publication date: 3-Jul-2024
https://doi.org/10.1088/1361-6501/ad5ab8
Gong XZhang TChen CLiu Z(2022)Research Review for Broad Learning System: Algorithms, Theory, and ApplicationsIEEE Transactions on Cybernetics10.1109/TCYB.2021.306109452:9(8922-8950)Online publication date: Sep-2022
https://doi.org/10.1109/TCYB.2021.3061094
Show More Cited By

Index Terms

A Flexible Approach for Human Activity Recognition Based on Broad Learning System
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Cognitive robotics
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

Recommendations

Continual learning in sensor-based human activity recognition: An empirical benchmark analysis
Abstract
Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and ...
Wearable-based behaviour interpolation for semi-supervised human activity recognition
Abstract
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-and-error process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep ...
CSI-based cross-scene human activity recognition with incremental learning
Abstract
Human Activity Recognition (HAR) based on Channel State Information (CSI) has important application prospects in various fields such as human–computer interactivity, medical health et al. Although the current CSI-based HAR researches have made ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

February 2019

563 pages

ISBN:9781450366007

DOI:10.1145/3318299

Copyright © 2019 ACM.

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

Southwest Jiaotong University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMLC '19

ICMLC '19: 2019 11th International Conference on Machine Learning and Computing

February 22 - 24, 2019

Zhuhai, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
153
Total Downloads

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

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Velayudhan DAhmed AHassan TOwais MGour NBennamoun MDamiani EWerghi N(2024)Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage itemsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-024-00182-77:1Online publication date: 24-Dec-2024
https://doi.org/10.1186/s42492-024-00182-7
Ni MLi S(2024)A data-driven LSTMSCBLS model for soft sensor of industrial processMeasurement Science and Technology10.1088/1361-6501/ad5ab835:10(106201)Online publication date: 3-Jul-2024
https://doi.org/10.1088/1361-6501/ad5ab8
Gong XZhang TChen CLiu Z(2022)Research Review for Broad Learning System: Algorithms, Theory, and ApplicationsIEEE Transactions on Cybernetics10.1109/TCYB.2021.306109452:9(8922-8950)Online publication date: Sep-2022
https://doi.org/10.1109/TCYB.2021.3061094
Zhang XWang ZMa JLi SSuo M(2021)State-of-Health Prediction for Reaction Wheel of On-Orbit Satellite Based on Fourier Broad Learning SystemIEEE Access10.1109/ACCESS.2021.31115929(125691-125705)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3111592
Xiao JChen LChen HHong X(2021)Baseline Model Training in Sensor-Based Human Activity Recognition: An Incremental Learning ApproachIEEE Access10.1109/ACCESS.2021.30777649(70261-70272)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3077764
Chaurasia SReddy S(2021)State-of-the-art survey on activity recognition and classification using smartphones and wearable sensorsMultimedia Tools and Applications10.1007/s11042-021-11410-0Online publication date: 22-Sep-2021
https://doi.org/10.1007/s11042-021-11410-0

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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten