Multi-input CNN-GRU based human activity recognition using wearable sensors
Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope an...
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| Vydané v: | Computing Ročník 103; číslo 7; s. 1461 - 1478 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Vienna
Springer Vienna
01.07.2021
Springer Nature B.V |
| Predmet: | |
| ISSN: | 0010-485X, 1436-5057 |
| On-line prístup: | Získať plný text |
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| Abstract | Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures. |
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| AbstractList | Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures. |
| Author | Semwal, Vijay Bhaskar Dua, Nidhi Singh, Shiva Nand |
| Author_xml | – sequence: 1 givenname: Nidhi orcidid: 0000-0001-9812-9141 surname: Dua fullname: Dua, Nidhi email: 2016rsec001@nitjsr.ac.in organization: Department of ECE, NIT Jamshedpur – sequence: 2 givenname: Shiva Nand surname: Singh fullname: Singh, Shiva Nand organization: Department of ECE, NIT Jamshedpur – sequence: 3 givenname: Vijay Bhaskar surname: Semwal fullname: Semwal, Vijay Bhaskar organization: Department of CSE, MANIT Bhopal |
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| Cites_doi | 10.1155/2018/7316954 10.1007/s11042-015-2643-0 10.1016/j.inffus.2017.01.004 10.1007/978-3-030-48849-9_12 10.1109/JSEN.2016.2628346 10.1007/s00607-018-0677-7 10.1007/s00521-015-2089-3 10.1016/j.eswa.2018.03.056 10.1109/JSEN.2016.2519679 10.1007/s00521-016-2744-3 10.1007/978-981-15-3357-0_14 10.1109/THMS.2018.2884717 10.1016/j.asoc.2017.09.027 10.1016/j.neucom.2019.07.034 10.1109/TIE.2018.2864702 10.1109/JSEN.2016.2570281 10.1109/ACCESS.2017.2779939 10.1007/s00607-012-0216-x 10.1007/s00607-019-00745-0 10.1007/s00607-018-0609-6 10.1109/ACCESS.2018.2890675 10.1007/s11042-019-08463-7 10.1109/JSEN.2017.2782492 10.1007/s11036-019-01445-x 10.1109/JIOT.2020.2995940 10.1109/72.279181 10.1109/ACCESS.2018.2873315 10.1016/j.eswa.2016.04.032 10.1007/978-981-13-0923-6_12 10.1109/ACCESS.2019.2920969 10.1109/ACCESS.2020.2982225 10.1145/1964897.1964918 10.1145/1553374.1553453 10.1109/ICAIIC48513.2020.9065078 10.1109/EUVIP47703.2019.8946180 10.1145/3267242.3267286 10.3115/v1/W14-4012 10.1109/ICASERT.2019.8934463 10.1109/ISWC.2012.13 10.5772/intechopen.81170 10.2991/icaita-16.2016.13 10.1109/ICMCCE.2018.00052 10.1109/TENCON.2016.7848159 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021. |
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| References | Zhao, Yang, Chevalier, Xu, Zhang (CR26) 2018 Guo, Wang, Yang, Li, An (CR7) 2018; 49 Yang, Raymond, Zhang, Wan, Long (CR50) 2018; 6 Semwal, Mondal, Nandi (CR11) 2017; 28 CR39 Yu, Dong (CR10) 2018; 100 CR33 Raj, Semwal, Nandi (CR44) 2018; 30 Khan, Sohn (CR1) 2013; 95 Ignatov (CR20) 2018; 62 Canizo, Triguero, Conde, Onieva (CR30) 2019; 363 CR4 CR3 Gupta, Semwal (CR43) 2020 Bengio, Simard, Frasconi (CR47) 1994; 5 CR49 CR48 Semwal, Gaud, Nandi (CR34) 2019 CR46 CR45 Zhang, Zhang, Hu (CR9) 2019; 101 CR41 Kwapisz, Weiss, Moore (CR23) 2011; 12 Quaid, Jalal (CR36) 2020; 79 Ronao, Cho (CR21) 2016; 59 Wang, Wu, Gravina, Fortino, Jiang, Tang (CR35) 2017; 37 Karim, Majumdar, Darabi, Chen (CR31) 2017; 6 CR19 CR16 CR15 CR14 Cornacchia, Ozcan, Zheng, Velipasalar (CR2) 2016; 17 CR12 CR52 CR51 Xu, Chai, He, Zhang, Duan (CR27) 2019; 7 Zhang, Zhang, Zhang, Bao, Zhang, Deng (CR37) 2019; 7 Lu, Zheng, Sheng, Jin, Yu (CR38) 2020; 7 Nweke, Teh, Al-Garadi, Alo (CR8) 2018; 105 Kaushik, Choudhury, Dasgupta, Natarajan, Pickett, Dutt (CR32) 2020 Liu, Hsaio, Tu (CR18) 2018; 66 Semwal, Nandi (CR42) 2016; 16 CR28 Wan, Qi, Xu, Tong, Gu (CR17) 2020; 25 CR25 Jain, Kanhangad (CR5) 2017; 18 CR24 CR22 Xia, Huang, Wang (CR29) 2020; 8 Ignatov, Strijov (CR40) 2016; 75 Wang, Wu, Chen, Ghoneim, Hossain (CR6) 2016; 16 Al-Makhadmeh, Tolba (CR13) 2020; 102 X Yu (928_CR10) 2018; 100 JR Kwapisz (928_CR23) 2011; 12 Z Yang (928_CR50) 2018; 6 Y Zhang (928_CR37) 2019; 7 MA Quaid (928_CR36) 2020; 79 K Xia (928_CR29) 2020; 8 928_CR33 Y Bengio (928_CR47) 1994; 5 928_CR39 CA Ronao (928_CR21) 2016; 59 AD Ignatov (928_CR40) 2016; 75 A Jain (928_CR5) 2017; 18 Z Al-Makhadmeh (928_CR13) 2020; 102 J Lu (928_CR38) 2020; 7 A Ignatov (928_CR20) 2018; 62 928_CR41 928_CR49 X Zhang (928_CR9) 2019; 101 VB Semwal (928_CR34) 2019 928_CR45 928_CR46 M Cornacchia (928_CR2) 2016; 17 928_CR48 M Raj (928_CR44) 2018; 30 M Guo (928_CR7) 2018; 49 VB Semwal (928_CR11) 2017; 28 C Xu (928_CR27) 2019; 7 HF Nweke (928_CR8) 2018; 105 VB Semwal (928_CR42) 2016; 16 A Gupta (928_CR43) 2020 928_CR52 S Wan (928_CR17) 2020; 25 F Karim (928_CR31) 2017; 6 928_CR51 928_CR16 928_CR19 928_CR12 928_CR14 928_CR15 M Canizo (928_CR30) 2019; 363 S Kaushik (928_CR32) 2020 928_CR22 Z Wang (928_CR6) 2016; 16 CL Liu (928_CR18) 2018; 66 ZA Khan (928_CR1) 2013; 95 928_CR28 928_CR3 928_CR4 928_CR24 Z Wang (928_CR35) 2017; 37 928_CR25 Y Zhao (928_CR26) 2018 |
| References_xml | – ident: CR45 – ident: CR22 – year: 2018 ident: CR26 article-title: Deep residual bidir-LSTM for human activity recognition using wearable sensors publication-title: Math Problems Eng doi: 10.1155/2018/7316954 – volume: 75 start-page: 7257 issue: 12 year: 2016 end-page: 7270 ident: CR40 article-title: Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer publication-title: Multimed Tools Appl doi: 10.1007/s11042-015-2643-0 – volume: 37 start-page: 1 year: 2017 end-page: 9 ident: CR35 article-title: Kernel fusion based extreme learning machine for cross-location activity recognition publication-title: Information Fusion doi: 10.1016/j.inffus.2017.01.004 – ident: CR49 – start-page: 185 year: 2020 end-page: 197 ident: CR43 article-title: Multiple task human gait analysis and identification: ensemble learning approach publication-title: Emotion and Information Processing doi: 10.1007/978-3-030-48849-9_12 – ident: CR4 – ident: CR39 – ident: CR16 – volume: 17 start-page: 386 year: 2016 end-page: 403 ident: CR2 article-title: A survey on activity detection and classification using wearable sensors publication-title: IEEE Sens J doi: 10.1109/JSEN.2016.2628346 – ident: CR51 – ident: CR12 – volume: 101 start-page: 637 issue: 6 year: 2019 end-page: 652 ident: CR9 article-title: Deep learning based vein segmentation from susceptibility-weighted images publication-title: Computing doi: 10.1007/s00607-018-0677-7 – volume: 28 start-page: 565 year: 2017 end-page: 574 ident: CR11 article-title: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach publication-title: Neural Comput Appl doi: 10.1007/s00521-015-2089-3 – volume: 105 start-page: 233 year: 2018 end-page: 261 ident: CR8 article-title: Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.03.056 – ident: CR25 – ident: CR46 – ident: CR19 – volume: 16 start-page: 3198 issue: 9 year: 2016 end-page: 3207 ident: CR6 article-title: A triaxial accelerometer-based human activity recognition via EEMD-based features and game-theory-based feature selection publication-title: IEEE Sens J. doi: 10.1109/JSEN.2016.2519679 – ident: CR15 – volume: 30 start-page: 1747 issue: 6 year: 2018 end-page: 1755 ident: CR44 article-title: Bidirectional association of joint angle trajectories for humanoid locomotion: the restricted Boltzmann machine approach publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2744-3 – start-page: 199 year: 2020 end-page: 216 ident: CR32 article-title: Ensemble of multi-headed machine learning architectures for time-series forecasting of healthcare expenditures publication-title: Applications of Machine Learning doi: 10.1007/978-981-15-3357-0_14 – volume: 49 start-page: 105 issue: 1 year: 2018 end-page: 111 ident: CR7 article-title: A multisensor multiclassifier hierarchical fusion model based on entropy weight for human activity recognition using wearable inertial sensors publication-title: IEEE Trans Hum–Mach Syst. doi: 10.1109/THMS.2018.2884717 – volume: 62 start-page: 915 year: 2018 end-page: 922 ident: CR20 article-title: Real-time human activity recognition from accelerometer data using Convolutional Neural Networks publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.09.027 – volume: 363 start-page: 246 year: 2019 end-page: 260 ident: CR30 article-title: Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.07.034 – volume: 66 start-page: 4788 issue: 6 year: 2018 end-page: 4797 ident: CR18 article-title: Time series classification with multivariate convolutional neural network publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2018.2864702 – volume: 16 start-page: 5805 issue: 14 year: 2016 end-page: 5816 ident: CR42 article-title: Generation of joint trajectories using hybrid automate-based model: a rocking block-based approach publication-title: IEEE Sens J doi: 10.1109/JSEN.2016.2570281 – volume: 6 start-page: 1662 year: 2017 end-page: 1669 ident: CR31 article-title: LSTM fully convolutional networks for time series classification publication-title: IEEE access doi: 10.1109/ACCESS.2017.2779939 – volume: 95 start-page: 109 year: 2013 end-page: 127 ident: CR1 article-title: A hierarchical abnormal human activity recognition system based on R-transform and kernel discriminant analysis for elderly health care publication-title: Computing doi: 10.1007/s00607-012-0216-x – volume: 102 start-page: 501 issue: 2 year: 2020 end-page: 522 ident: CR13 article-title: Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach publication-title: Computing doi: 10.1007/s00607-019-00745-0 – ident: CR14 – volume: 100 start-page: 773 issue: 8 year: 2018 end-page: 785 ident: CR10 article-title: PTL-CFS based deep convolutional neural network model for remote sensing classification publication-title: Computing doi: 10.1007/s00607-018-0609-6 – volume: 7 start-page: 9893 year: 2019 end-page: 9902 ident: CR27 article-title: InnoHAR: A deep neural network for complex human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2890675 – volume: 79 start-page: 6061 issue: 9 year: 2020 end-page: 6083 ident: CR36 article-title: Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm publication-title: Multimed Tools Appl doi: 10.1007/s11042-019-08463-7 – ident: CR33 – volume: 18 start-page: 1169 issue: 3 year: 2017 end-page: 1177 ident: CR5 article-title: Human activity classification in smartphones using accelerometer and gyroscope sensors publication-title: IEEE Sens J doi: 10.1109/JSEN.2017.2782492 – volume: 25 start-page: 743 issue: 2 year: 2020 end-page: 755 ident: CR17 article-title: Deep learning models for real-time human activity recognition with smartphones publication-title: Mobile Networks Appl doi: 10.1007/s11036-019-01445-x – volume: 7 start-page: 11137 issue: 11 year: 2020 end-page: 11146 ident: CR38 article-title: Efficient human activity recognition using a single wearable sensor publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2020.2995940 – volume: 5 start-page: 157 issue: 2 year: 1994 end-page: 166 ident: CR47 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans Neural Networks doi: 10.1109/72.279181 – volume: 6 start-page: 56750 year: 2018 end-page: 56764 ident: CR50 article-title: DFTerNet: Towards 2-bit dynamic fusion networks for accurate human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2873315 – volume: 59 start-page: 235 year: 2016 end-page: 244 ident: CR21 article-title: Human activity recognition with smartphone sensors using deep learning neural networks publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.04.032 – start-page: 135 year: 2019 end-page: 145 ident: CR34 article-title: Human gait state prediction using cellular automata and classification using ELM publication-title: Machine intelligence and signal analysis doi: 10.1007/978-981-13-0923-6_12 – volume: 7 start-page: 75213 year: 2019 end-page: 75226 ident: CR37 article-title: Human activity recognition based on motion sensor using u-net publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2920969 – ident: CR48 – volume: 8 start-page: 56855 year: 2020 end-page: 56866 ident: CR29 article-title: LSTM-CNN architecture for human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2982225 – ident: CR3 – ident: CR52 – volume: 12 start-page: 74 issue: 2 year: 2011 end-page: 82 ident: CR23 article-title: Activity recognition using cell phone 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| SubjectTerms | Accelerometers Artificial Intelligence Artificial neural networks Classification Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Feature extraction Human activity recognition Information Systems Applications (incl.Internet) Moving object recognition Neural networks Regular Paper Sensors Software Engineering Wearable technology |
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