XGBoost-Based Human Activity Recognition Algorithm using Wearable Smart Devices

Research Article
Open access

XGBoost-Based Human Activity Recognition Algorithm using Wearable Smart Devices

Runchan Ge 1*
  • 1 Harbin Institute of Technology    
  • *corresponding author 1170300808@hit.edu.cn
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220514
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

With the popularity of wearable smart devices, human activity recognition (HAR) based on smart sensors has been widely applied in daily life and medical health fields. In order to balance the accuracy of HAR and the complexity of the algorithm, this paper proposes an HAR algorithm based on the extreme gradient boosting (XGBoost) method. The original data collected by sensors contains noise, thus denoising process is firstly performed. Then multi-dimensional features are extracted from the data because of the limited dimensional features, which cannot be directly utilized in the training process. After that, principal component analysis (PCA) is used to reduce the dimensionality of the data in order to alleviate the input complexity of the training model. Finally, the human activity is recognized using the XGBoost method, and the ultimate goal is to obtain the tradeoff between speed and accuracy of the HAR algorithm.

Keywords:

Feature extraction, PCA, human activity recognition, XGBoost.

Ge,R. (2023). XGBoost-Based Human Activity Recognition Algorithm using Wearable Smart Devices. Applied and Computational Engineering,2,403-409.
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References

[1]. Kuerbis A, Mulliken A, Muench F, et al. Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health[J]. 2017.

[2]. Wang Y, Cang S, Yu H. A survey on wearable sensor modality centred human activity recognition in health care[J]. Expert Systems with Applications, 2019, 137: 167-190.

[3]. Fan L, Wang Z. Human activity recognition model based on location-independent accelerometer[J]. Application Research of Computers, 2015, 32(1): 63-66

[4]. Balli S, Sağbaş E A, Peker M. Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm[J]. Measurement and Control, 2019, 52(1-2): 37-45.

[5]. ZHU Xiangbin, QIU Huiling. Human activity recognition with smartphone sensor data. Computer Engineering and Applications, 2016, 52（23）:1-5

[6]. Guo Yuanbo, Kong Jing, Liu Chunhui, Wang Yifeng. Activity recognition based on the alteration of multi-sensor in smart phone [J].Journal on Communications,2018,39(S2):164-172.

[7]. Abdul Rehman Javed, Muhammad Usman Sarwar, Suleman Khan, Celestine Iwendi, Mohit Mittal, Neeraj Kumar. Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition[J]. Sensors,2020,20(8):

[8]. H. Chen, S. Mahfuz and F. Zulkernine, "Smart Phone Based Human Activity Recognition," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019, pp. 2525-2532, doi: 10.1109/BIBM47256.2019.8983009.

[9]. Mohammed Mehedi Hassan,Md. Zia Uddin,Amr Mohamed,Ahmad Almogren. A robust human activity recognition system using smartphone sensors and deep learning[J]. Future Generation Computer Systems,2018,81:

[10]. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

[11]. Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.

[12]. G. Vavoulas, C. Chatzaki, T. Malliotakis, M. Pediaditis, and M. Tsiknakis. 2016. The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones. Proceedings of The International Conference On Information and Communication Technologies for Ageing Well And E-Health.

[13]. Ogunleye A, Wang Q G. XGBoost model for chronic kidney disease diagnosis[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2019, 17(6): 2131-2140.

[14]. Torlay L, Perrone-Bertolotti M, Thomas E, et al. Machine learning–XGBoost analysis of language networks to classify patients with epilepsy[J]. Brain informatics, 2017, 4(3): 159-169.

Cite this article

Ge,R. (2023). XGBoost-Based Human Activity Recognition Algorithm using Wearable Smart Devices. Applied and Computational Engineering,2,403-409.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Kuerbis A, Mulliken A, Muench F, et al. Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health[J]. 2017.

[2]. Wang Y, Cang S, Yu H. A survey on wearable sensor modality centred human activity recognition in health care[J]. Expert Systems with Applications, 2019, 137: 167-190.

[3]. Fan L, Wang Z. Human activity recognition model based on location-independent accelerometer[J]. Application Research of Computers, 2015, 32(1): 63-66

[4]. Balli S, Sağbaş E A, Peker M. Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm[J]. Measurement and Control, 2019, 52(1-2): 37-45.

[5]. ZHU Xiangbin, QIU Huiling. Human activity recognition with smartphone sensor data. Computer Engineering and Applications, 2016, 52（23）:1-5

[6]. Guo Yuanbo, Kong Jing, Liu Chunhui, Wang Yifeng. Activity recognition based on the alteration of multi-sensor in smart phone [J].Journal on Communications,2018,39(S2):164-172.

[7]. Abdul Rehman Javed, Muhammad Usman Sarwar, Suleman Khan, Celestine Iwendi, Mohit Mittal, Neeraj Kumar. Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition[J]. Sensors,2020,20(8):

[8]. H. Chen, S. Mahfuz and F. Zulkernine, "Smart Phone Based Human Activity Recognition," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019, pp. 2525-2532, doi: 10.1109/BIBM47256.2019.8983009.

[9]. Mohammed Mehedi Hassan,Md. Zia Uddin,Amr Mohamed,Ahmad Almogren. A robust human activity recognition system using smartphone sensors and deep learning[J]. Future Generation Computer Systems,2018,81:

[10]. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

[11]. Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794.

[12]. G. Vavoulas, C. Chatzaki, T. Malliotakis, M. Pediaditis, and M. Tsiknakis. 2016. The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones. Proceedings of The International Conference On Information and Communication Technologies for Ageing Well And E-Health.

[13]. Ogunleye A, Wang Q G. XGBoost model for chronic kidney disease diagnosis[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2019, 17(6): 2131-2140.

[14]. Torlay L, Perrone-Bertolotti M, Thomas E, et al. Machine learning–XGBoost analysis of language networks to classify patients with epilepsy[J]. Brain informatics, 2017, 4(3): 159-169.

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