Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

Tamilvizhi Thanarajan; Youseef Alotaibi; Surendran Rajendran; Krishnaraj Nagappan; Tamilvizhi Thanarajan; Youseef Alotaibi; Surendran Rajendran; Krishnaraj Nagappan

doi:10.3934/math.2023629

AIMS Mathematics

2023, Volume 8, Issue 5: 12520-12539. doi: 10.3934/math.2023629

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Research article

Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

1.
Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
2.
Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
3.
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
4.
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, India

Received: 20 January 2023 Revised: 04 March 2023 Accepted: 17 March 2023 Published: 27 March 2023
MSC : 68Q32, 68T40, 90C25, 92D30

A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models.
- human activity recognition,
- deep learning,
- healthcare,
- parameter tuning,
- dimensionality reduction
Citation: Tamilvizhi Thanarajan, Youseef Alotaibi, Surendran Rajendran, Krishnaraj Nagappan. Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition[J]. AIMS Mathematics, 2023, 8(5): 12520-12539. doi: 10.3934/math.2023629

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Abstract

A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models.

References

[1]	Y. Wang, S. Cang, H. Yu, A survey on wearable sensor modality centred human activity recognition in health care, Expert Syst. Appl., 137 (2019), 167–190. https://doi.org/10.1016/j.eswa.2019.04.057 doi: 10.1016/j.eswa.2019.04.057
[2]	L. M. Dang, K. Min, H. Wang, M. J. Piran, C. H. Lee, H. Moon, Sensor-based and vision-based human activity recognition: A comprehensive survey, Pattern Recogn., 108 (2020), 107561. https://doi.org/10.1016/j.patcog.2020.107561 doi: 10.1016/j.patcog.2020.107561
[3]	K. A. Ogudo, R. Surendran, O. I. Khalaf, Optimal artificial intelligence based automated skin lesion detection and classification model, Comput. Syst. Sci. Eng., 44 (2023), 693–707. https://doi.org/10.32604/csse.2023.024154 doi: 10.32604/csse.2023.024154
[4]	A. Subasi, M. Radhwan, R. Kurdi, K. Khateeb, IoT based mobile healthcare system for human activity recognition, Proceedings of the 15th learning and technology conference (L & T), Jeddah, Saudi Arabia, (2018), 29–34. https://doi.org/10.1109/LT.2018.8368507
[5]	N. Ahmed, J. I. Rafiq, M. R. Islam, Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model, Sensors, 20 (2020), 317. https://doi.org/10.3390/s20010317 doi: 10.3390/s20010317
[6]	W. Taylor, S. A. Shah, K. Dashtipour, A. Zahid, Q. H. Abbasi, M. A. Imran, An intelligent non-invasive real-time human activity recognition system for next-generation healthcare, Sensors, 20 (2020), 2653. https://doi.org/10.3390/s20092653 doi: 10.3390/s20092653
[7]	S. Mekruksavanich, A. Jitpattanakul, Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models, Electronics, 10 (2021), 308. https://doi.org/10.3390/electronics10030308 doi: 10.3390/electronics10030308
[8]	V. Bianchi, M. Bassoli, G. Lombardo, P. Fornacciari, M. Mordonini, I. De Munari, IoT wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment, IEEE Internet Things, 6 (2019), 8553–8562. https://doi.org/10.1109/JIOT.2019.2920283 doi: 10.1109/JIOT.2019.2920283
[9]	N. Golestani, M. Moghaddam, Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks, Nat. Commun., 11 (2020), 1–11. https://doi.org/10.1038/s41467-020-15086-2 doi: 10.1038/s41467-020-15086-2
[10]	B. Vidya, P. Sasikumar, Wearable multisensor data fusion approach for human activity recognition using machine learning algorithms, Sensor. Actuat. A-Phys., 341 (2022), 113557. https://doi.org/10.1016/j.sna.2022.113557 doi: 10.1016/j.sna.2022.113557
[11]	M. M. Hassan, M. Z. Uddin, A. Mohamed, A. Almogren, A robust human activity recognition system using smartphone sensors and deep learning, Future Gener. Comp. Sy., 81 (2018), 307–313. https://doi.org/10.1016/j.future.2017.11.029 doi: 10.1016/j.future.2017.11.029
[12]	Y. Jia, Y. Guo, G. Wang, R. Song, G. Cui, X. Zhong, Multi-frequency and multidomain human activity recognition based on SFCW radar using deep learning, Neurocomputing, 444 (2021), 274–287. https://doi.org/10.1016/j.neucom.2020.07.136 doi: 10.1016/j.neucom.2020.07.136
[13]	X. Zhou, W. Liang, I. Kevin, K. Wang, H. Wang, L. T. Yang, et al., Deep-learning-enhanced human activity recognition for Internet of healthcare things, IEEE Internet Things, 7 (2020), 6429–6438. https://doi.org/10.1109/JIOT.2020.2985082 doi: 10.1109/JIOT.2020.2985082
[14]	A. Gumaei, M. M. Hassan, A. Alelaiwi, H. Alsalman, A hybrid deep learning model for human activity recognition using multimodal body sensing data, IEEE Access, 7 (2019), 99152–99160. https://doi.org/10.1109/ACCESS.2019.2927134 doi: 10.1109/ACCESS.2019.2927134
[15]	Z. Chen, L. Zhang, C. Jiang, Z. Cao, W. Cui, WiFi CSI based passive human activity recognition using attention-based BLSTM, IEEE T. Mobile Comput., 18 (2018), 2714–2724. https://doi.org/10.1109/TMC.2018.2878233 doi: 10.1109/TMC.2018.2878233
[16]	D. Thakur, S. Biswas, E. S. Ho, S. Chattopadhyay, Convae-lstm: Convolutional autoencoder long short-term memory network for smartphone-based human activity recognition, IEEE Access, 10 (2022), 4137–4156. https://doi.org/10.1109/ACCESS.2022.3140373 doi: 10.1109/ACCESS.2022.3140373
[17]	S. Wan, L. Qi, X. Xu, C. Tong, Z. Gu, Deep learning models for real-time human activity recognition on smartphones, Mobile Netw. Appl., 25 (2020), 743–755. https://doi.org/10.1007/s11036-019-01445-x doi: 10.1007/s11036-019-01445-x
[18]	H. Guan, B. Tang, X. Zhou, H. Tan, Z. Liang, Y. Li, et al., Reliability analysis of boom of new lifting equipment based on improved wolf swarm algorithm, J. Eng., 2023 (2023). https://doi.org/10.1049/tje2.12202
[19]	A. A. Malibari, S. S. Alotaibi, R. Alshahrani, S. Dhahbi, R. Alabdan, F. N. Al-wesabi, et al., A novel metaheuristic with deep learning enabled intrusion detection system for secured smart environment, Sustain. Energy Techn., 52 (2022), 102312. https://doi.org/10.1016/j.seta.2022.102312 doi: 10.1016/j.seta.2022.102312
[20]	M. H. Alharbi, A. N. Alqefari, Y. A. Alhawday, A. F. Alghammas, A. Hershan, Association of menstrual and reproductive factors with thyroid cancer in Saudi female patients, J. Umm Al-Qura University Medical Sci., 7 (2021), 11–13. https://doi.org/10.54940/ms81150310 doi: 10.54940/ms81150310
[21]	F. Alrowais, S. Althahabi, S. Alotaibi, A. Mohamed, M. A. Hamza, Automated machine learning enabled cyber security threat detection in Internet of things environment, Comput. Syst. Sci. Eng., 45 (2023), 687–700. https://doi.org/10.32604/csse.2023.030188 doi: 10.32604/csse.2023.030188
[22]	S. Rajagopal, T. Thanarajan, Y. Alotaibi, S. Alghamdi, Brain tumor: Hybrid feature extraction based on UNET and 3DCNN, Comput. Syst. Sci. Eng., 45 (2023), 2093–2109. https://doi.org/10.32604/csse.2023.032488 doi: 10.32604/csse.2023.032488
[23]	K. Nagappan, S. Rajendran, Y. Alotaibi, Trust aware Multi-Objective metaheuristic Optimization-Based secure route planning technique for Cluster-Based IIoT environment, IEEE Access, 10 (2022), 112686–112694. https://doi.org/10.1109/ACCESS.2022.3211971 doi: 10.1109/ACCESS.2022.3211971
[24]	M. A. Duhayyim, A. A. Malibari, S. Dhahbi, M. K. Nour, I. Al-Turaiki, Sailfish optimization with deep learning based oral cancer classification model, Comput. Syst. Sci. Eng., 45 (2023), 753–767. https://doi.org/10.32604/csse.2023.030556 doi: 10.32604/csse.2023.030556
[25]	R. Edwards, M. Wood, Branch prioritization motifs in biochemical networks with sharp activation. AIMS Math., 7 (2022), 1115–1146. https://doi.org/10.3934/math.2022066 doi: 10.3934/math.2022066
[26]	A. Q. Khan, Z. Saleem, T. F. Ibrahim, K. Osman, F. M. Alshehri, M. A. El-Moneam, Bifurcation and chaos in a discrete activator-inhibitor system, AIMS Math., 8 (2023), 4551–4574. https://doi.org/10.3934/math.2023225 doi: 10.3934/math.2023225
[27]	https://archive.ics.uci.edu/ml/datasets/Smartphone+Dataset+for+Human+Activity+Recognition+%28HAR%29+in+Ambient+Assisted+Living+%28AAL%29
[28]	Y. Tang, L. Zhang, F. Min, J. He, Multiscale deep feature learning for human activity recognition using wearable sensors, IEEE T. Ind. Electron., 70 (2023), 2106–2116. https://doi.org/10.1109/TIE.2022.3161812 doi: 10.1109/TIE.2022.3161812
[29]	T. Tamilvizhi, R. Surendran, K. Anbazhagan, K. Rajkumar, Quantum behaved particle swarm Optimization-Based deep transfer learning model for sugarcane leaf disease detection and classification, Math. Probl. Eng., 2022 (2022), 3452413. https://doi.org/10.1155/2022/3452413 doi: 10.1155/2022/3452413
[30]	C. Han, L. Zhang, Y. Tang, W. Huang, F. Min, J. He, Human activity recognition using wearable sensors by heterogeneous convolutional neural networks, Expert Syst. Appl., 198 (2022), 116764. https://doi.org/10.1016/j.eswa.2022.116764 doi: 10.1016/j.eswa.2022.116764
[31]	K. Wang, J. He, L. Zhang, Sequential weakly labeled multiactivity localization and recognition on wearable sensors using recurrent attention networks, IEEE T. Hum-Mach. Syst., 51 (2021), 355–364. https://doi.org/10.48550/arXiv.2004.05768 doi: 10.48550/arXiv.2004.05768

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