A Survey of Motion Data Processing and Classification Techniques Based on Wearable Sensors

Xiaoqiong Xiong; Xuemei Xiong; Chao Lian; Keda Zeng

doi:10.61927/igmin123

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The Educational Role of Cinema in Physical Sciences

Maria Sagri, Denis Vavougios and Filippos Sofos

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Roadmap for Renewable Energy Development

Penjiyev Ahmet Myradovich

Engineering Group Review Article 記事ID: igmin123

A Survey of Motion Data Processing and Classification Techniques Based on Wearable Sensors

Machine Learning

Xiaoqiong Xiong ,

Xuemei Xiong ,

Chao Lian ^* and

Keda Zeng

Affiliation

School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

DOI10.61927/igmin123 全文HTML 全文PDF この記事を引用する

要約

The rapid development of wearable technology provides new opportunities for action data processing and classification techniques. Wearable sensors can monitor the physiological and motion signals of the human body in real-time, providing rich data sources for health monitoring, sports analysis, and human-computer interaction. This paper provides a comprehensive review of motion data processing and classification techniques based on wearable sensors, mainly including feature extraction techniques, classification techniques, and future development and challenges. First, this paper introduces the research background of wearable sensors, emphasizing their important applications in health monitoring, sports analysis, and human-computer interaction. Then, it elaborates on the work content of action data processing and classification techniques, including feature extraction, model construction, and activity recognition. In feature extraction techniques, this paper focuses on the content of shallow feature extraction and deep feature extraction; in classification techniques, it mainly studies traditional machine learning models and deep learning models. Finally, this paper points out the current challenges and prospects for future research directions. Through in-depth discussions of feature extraction techniques and classification techniques for sensor time series data in wearable technology, this paper helps promote the application and development of wearable technology in health monitoring, sports analysis, and human-computer interaction.
Index Terms: Activity recognition, Wearable sensor, Feature extraction, Classification

数字

参考文献

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[1] Zhang S, Li Y, Zhang S, Shahabi F, Xia S, Deng Y, Alshurafa N. Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances. Sensors (Basel). 2022 Feb 14;22(4):1476. doi: 10.3390/s22041476. PMID: 35214377; PMCID: PMC8879042.

[2] Hu Y, Yan Y. A Wearable Electrostatic Sensor for Human Activity Monitoring. IEEE Trans Instrum Meas. 2022; 71.

[3] Verma U, Tyagi P, Kaur M. “Artificial intelligence in human activity recognition: a review.” International Journal of Sensor Networks. 2023; 41:1; 1.

[4] Peng Z, Zheng S, Zhang X, Yang J, Wu S, Ding C, Lei L, Chen L, Feng G. Flexible Wearable Pressure Sensor Based on Collagen Fiber Material. Micromachines (Basel). 2022 Apr 28;13(5):694. doi: 10.3390/mi13050694. PMID: 35630161; PMCID: PMC9143406.

[5] Ige AO, Noor MMH. A survey on unsupervised learning for wearable sensor-based activity recognition. Appl Soft Comput. 2022; 127: 109363.

[6] Kristoffersson A, Lindén M. A Systematic Review of Wearable Sensors for Monitoring Physical Activity. Sensors (Basel). 2022 Jan 12;22(2):573. doi: 10.3390/s22020573. PMID: 35062531; PMCID: PMC8778538.

[7] Kumar R, Kumar S. Survey on artificial intelligence-based human action recognition in video sequences. 2023; 62:2; 023102.

[8] Putra PU, Shima K, Shimatani K. A deep neural network model for multi-view human activity recognition. PLoS One. 2022 Jan 7;17(1):e0262181. doi: 10.1371/journal.pone.0262181. PMID: 34995315; PMCID: PMC8741063.

[9] Meratwal M, Spicher N, Deserno TM. “Multi-camera and multi-person indoor activity recognition for continuous health monitoring using long short term memory.” 2022; 12037: 64-71.

[10] Deotale D. HARTIV: Human Activity Recognition Using Temporal Information in Videos. Computers, Materials & Continua. 2021; 70: 2;3919-3938.

[11] Jin Z, Li Z, Gan T, Fu Z, Zhang C, He Z, Zhang H, Wang P, Liu J, Ye X. A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition. Sensors (Basel). 2022 May 5;22(9):3524. doi: 10.3390/s22093524. PMID: 35591215; PMCID: PMC9105339.

[12] Jang Y, Jeong I, Younesi Heravi M, Sarkar S, Shin H, Ahn Y. Multi-Camera-Based Human Activity Recognition for Human-Robot Collaboration in Construction. Sensors (Basel). 2023 Aug 7;23(15):6997. doi: 10.3390/s23156997. PMID: 37571779; PMCID: PMC10422633.

[13] Bernaś M, Płaczek B, Lewandowski M. Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes. Sensors (Basel). 2022 Dec 3;22(23):9451. doi: 10.3390/s22239451. PMID: 36502154; PMCID: PMC9739648.

[14] Papel JF, Munaka T. Home Activity Recognition by Sounds of Daily Life Using Improved Feature Extraction Method. IEICE Trans Inf Syst. 2023; E106.D:4;450-458

[15] Wang X, Shang J. Human Activity Recognition Based on Two-Channel Residual–GRU–ECA Module with Two Types of Sensors. 2023; 12: 1622.

[16] Qin Z, Zhang Y, Meng S, Qin Z, Choo KKR. Imaging and fusing time series for wearable sensor-based human activity recognition. Information Fusion. 2020; 53:80-87.

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