Human activity recognition based on hybrid learning algorithm for wearable sensor data
Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessi...
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| Vydané v: | Measurement. Sensors Ročník 24; s. 100512 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.12.2022
Elsevier |
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| ISSN: | 2665-9174, 2665-9174 |
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| Abstract | Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessitates the constant usage of smartphones, smartwatches, and wearable devices to capture patients' daily activities. To predict multiple human activities, deep learning (DL)-based methods have been successfully applied to time-series data that are generated by smartphones and wearable sensors. Although DL-based approaches were deployed in activity recognition, they still have encountered a few issues when working with time-series data. Those issues could be managed with the proposed methodology. This work proposed a couple of Hybrid Learning Algorithms (HLA) to build comprehensive classification methods for HAR using wearable sensor data. The aim of this work is to make use of the Convolution Memory Fusion Algorithm(CMFA) and Convolution Gated Fusion Algorithm(CGFA) that model learns both local features and long-term and gated-term dependencies in sequential data. Feature extraction has been enhanced with the deployment of various filter sizes. They are used to capture different local temporal dependencies, and thus the enhancement is implemented. This Amalgam Learning Model has been deployed on the WISDM dataset, and the proposed models have achieved 97.76%, 94.98% for smartwatch and smartphone of CMFA, 96.91%, 84.35% for smartwatch and smartphone of CGFA. Experimental results show that these models demonstrated greater accuracy than other existing deep neural network frameworks. |
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| AbstractList | Human Activity Recognition (HAR), based on sensor devices and the Internet of Things (IoT), attracted many researchers since it has diversified applications in health sectors, smart environments, and entertainment. HAR has emerged as one of the important health monitoring applications and it necessitates the constant usage of smartphones, smartwatches, and wearable devices to capture patients' daily activities. To predict multiple human activities, deep learning (DL)-based methods have been successfully applied to time-series data that are generated by smartphones and wearable sensors. Although DL-based approaches were deployed in activity recognition, they still have encountered a few issues when working with time-series data. Those issues could be managed with the proposed methodology. This work proposed a couple of Hybrid Learning Algorithms (HLA) to build comprehensive classification methods for HAR using wearable sensor data. The aim of this work is to make use of the Convolution Memory Fusion Algorithm(CMFA) and Convolution Gated Fusion Algorithm(CGFA) that model learns both local features and long-term and gated-term dependencies in sequential data. Feature extraction has been enhanced with the deployment of various filter sizes. They are used to capture different local temporal dependencies, and thus the enhancement is implemented. This Amalgam Learning Model has been deployed on the WISDM dataset, and the proposed models have achieved 97.76%, 94.98% for smartwatch and smartphone of CMFA, 96.91%, 84.35% for smartwatch and smartphone of CGFA. Experimental results show that these models demonstrated greater accuracy than other existing deep neural network frameworks. |
| ArticleNumber | 100512 |
| Author | Athota, Ravi Kumar Sumathi, D. |
| Author_xml | – sequence: 1 givenname: Ravi Kumar orcidid: 0000-0002-6838-2397 surname: Athota fullname: Athota, Ravi Kumar email: athotaravikumar@gmail.com, ravikumar.20phd7109@vitap.ac.in organization: Ph.D. Research Scholar, School of Computer Science and Engineering, VIT-AP University, Vijayawada, AP, India – sequence: 2 givenname: D. surname: Sumathi fullname: Sumathi, D. email: sumathi.research28@gmail.com organization: Associate Professor Grade-2, School of Computer Science and Engineering, VIT-AP University, Vijayawada, AP, India |
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| Cites_doi | 10.3390/fi11120259 10.1155/2018/7316954 10.1109/JSEN.2019.2928502 10.1016/j.dcan.2015.02.006 10.1109/JSEN.2020.3027097 10.1007/s11370-020-00343-6 10.1109/JSEN.2019.2917225 10.1109/ACCESS.2020.2982225 10.1109/THMS.2021.3086008 10.3390/sym12091570 10.1109/ACCESS.2019.2940729 10.1007/s00521-015-2089-3 10.1109/ACCESS.2020.2971693 10.1155/2016/4073584 10.1049/ell2.12062 10.1109/SURV.2012.110112.00192 10.1109/ACCESS.2019.2906663 10.1109/TCSVT.2008.2005594 10.1109/JSEN.2020.2978772 10.3390/s16020184 10.3390/s19071644 10.4304/jnw.4.10.976-984 10.1109/ACCESS.2020.2984214 10.1016/j.asoc.2017.09.027 10.1109/ACCESS.2016.2557846 10.1016/j.jjimei.2021.100046 10.3389/frobt.2015.00028 10.1289/ehp.5350 10.1007/s11036-019-01445-x 10.1007/s00371-019-01775-7 10.1006/cviu.1998.0744 10.3390/electronics10030308 10.3390/sym8100100 10.1007/s00779-003-0240-0 10.1016/j.procs.2019.08.100 10.1016/0021-9681(62)90117-0 10.3390/s21051636 10.1016/j.procs.2018.04.095 10.1016/j.patrec.2018.02.010 10.3758/BF03212378 10.3390/s16010115 |
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| Keywords | Hybrid learning algorithm Sensors Human activity recognition Convolution neural network Convolution memory fusion algorithm Convolution gated fusion algorithm |
| Language | English |
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| References | Mekruksavanich, Jitpattanakul (bib56) 2021; 10 Santos, Endo, Monteiro, Rocha, Silva, Lynn (bib18) 2019; 19 Žemgulys, Raudonis, Maskeliūnas, Damaševičius (bib4) 2018; 130 Ibrahim, Gomaa, Youssef (bib9) 2019; 19 Mozer (bib29) 1998, March; vol. 58 Chen, Zhang, Yao, Guo, Yu, Liu (bib23) 2021; 54 Katz, Jackson, Jaffe, Littell, Turk (bib13) 1962; 15 Panwar, Dyuthi, Prakash, Biswas, Acharyya, Maharatna, Naik (bib43) 2017, July Weiss, Yoneda, Hayajneh (bib16) 2019; 7 Tapia, Intille, Larson (bib28) 2004, April Xia, Huang, Wang (bib52) 2020; 8 Abdelnasser, Youssef, Harras (bib30) 2015, April Ilägcrstrand (bib35) 1970; vol. 24 Zhao, Yang, Chevalier, Xu, Zhang (bib50) 2018; 2018 Gani, Sarwar, Rahman (bib37) 2009; 4 Dewangan, Sahu (bib39) 2020; 21 Zhan, Nishimura, Kuroda (bib20) 2010; 130 Johansson (bib26) 1973; 14 Zhao, Yang, Chevalier, Xu, Zhang (bib64) 2018; 2018 Gupta (bib70) 2021; 1 Hussain, Sheng, Zhang (bib24) 2019 Damaševičius, Vasiljevas, Šalkevičius, Woźniak (bib2) 2016; 2016 Maskeliūnas, Damaševičius, Segal (bib3) 2019; 11 Han (bib6) 2021; 17 Wang, He, Zhang (bib53) 2021; 51 Vrigkas, Nikou, Kakadiaris (bib17) 2015; 2 Ignatov (bib15) 2018; 62 Mekruksavanich, Jitpattanakul, Youplao, Yupapin (bib19) 2020; 12 Damirchi, Khorrambakht, Taghirad (bib69) 2020, December Hernández, Suárez, Villamizar, Altuve (bib49) 2019, April Benegui, Ionescu (bib57) 2020; 8 Semwal, Mondal, Nandi (bib42) 2017; 28 Musale, Baek, Werellagama, Woo, Choi (bib60) 2019; 7 Wang, Chen, Hao, Peng, Hu (bib1) 2019; 119 Xi, Guan, Shu, Borgeat, Goubran (bib41) 2020; 36 Zeng, Nguyen, Yu, Mengshoel, Zhu, Wu, Zhang (bib47) 2014, November Weiss (bib22) 2019 Wang, He, Zhang (bib45) 2019; 19 Dang, Min, Wang, Piran, Lee, Moon (bib8) 2020; 108 Ullah, Ullah, Khan, Cheikh (bib63) 2019, October Mekruksavanich, Jitpattanakul (bib12) 2021; 10 Wang, He, Zhang (bib67) 2021; 51 Turaga, Chellappa, Subrahmanian, Udrea (bib27) 2008; 18 Corbett (bib33) 2001 Damaševičius, Maskeliūnas, Venčkauskas, Woźniak (bib5) 2016; 8 Ashbrook, Starner (bib34) 2003; 7 Mutegeki, Han (bib51) 2020, February Dewangan, Sahu (bib40) 2021; 14 Mutegeki, Han (bib65) 2020, February Jobanputra, Bavishi, Doshi (bib7) 2019; 155 Mekruksavanich, Jitpattanakul (bib11) 2021; 21 Li, Liu, Zhang, Ni, Wang, Li (bib54) 2021 Aggarwal, Cai (bib25) 1999; 73 Seshadri, Li, Voos, Rowbottom, Alfes, Zorman, Drummond (bib32) 2019; 2 Ahmad, Alqarni, Khan, Khan, Hussain Chauhdary, Mazzara, Distefano (bib61) 2018 Zebin, Scully, Ozanyan (bib10) 2016, October Neverova, Wolf, Lacey, Fridman, Chandra, Barbello, Taylor (bib62) 2016; 4 Dewangan, Sahu (bib38) 2021; 57 Li, Liu, Zhang, Ni, Wang, Li (bib68) 2021 Ordóñez, Roggen (bib66) 2016; 16 Angrisano, Bernardi, Cimitile, Gaglione, Vultaggio (bib59) 2020; 8 Weiss, Yoneda, Hayajneh (bib58) 2019; 7 Pires, Garcia, Pombo, Flórez-Revuelta (bib14) 2016; 16 Wan, Qi, Xu, Tong, Gu (bib44) 2020; 25 Teng, Wang, Zhang, He (bib46) 2020; 20 Lara, Labrador (bib21) 2012; 15 Ullah, Ullah, Khan, Cheikh (bib48) 2019, October Wang, Zhou (bib31) 2015; 1 Parziale, Carmona-Duarte, Ferrer, Marcelli (bib55) 2021, September Elgethun, Fenske, Yost, Palcisko (bib36) 2003; 111 Abdelnasser (10.1016/j.measen.2022.100512_bib30) 2015 Wang (10.1016/j.measen.2022.100512_bib31) 2015; 1 Ordóñez (10.1016/j.measen.2022.100512_bib66) 2016; 16 Ahmad (10.1016/j.measen.2022.100512_bib61) 2018 Mekruksavanich (10.1016/j.measen.2022.100512_bib12) 2021; 10 Ashbrook (10.1016/j.measen.2022.100512_bib34) 2003; 7 Ilägcrstrand (10.1016/j.measen.2022.100512_bib35) 1970; vol. 24 Mutegeki (10.1016/j.measen.2022.100512_bib51) 2020 Maskeliūnas (10.1016/j.measen.2022.100512_bib3) 2019; 11 Ullah (10.1016/j.measen.2022.100512_bib63) 2019 Semwal (10.1016/j.measen.2022.100512_bib42) 2017; 28 Katz (10.1016/j.measen.2022.100512_bib13) 1962; 15 Zhao (10.1016/j.measen.2022.100512_bib50) 2018; 2018 Zebin (10.1016/j.measen.2022.100512_bib10) 2016 Wang (10.1016/j.measen.2022.100512_bib53) 2021; 51 Žemgulys (10.1016/j.measen.2022.100512_bib4) 2018; 130 Ibrahim (10.1016/j.measen.2022.100512_bib9) 2019; 19 Hussain (10.1016/j.measen.2022.100512_bib24) 2019 Xia (10.1016/j.measen.2022.100512_bib52) 2020; 8 Damaševičius (10.1016/j.measen.2022.100512_bib2) 2016; 2016 Gupta (10.1016/j.measen.2022.100512_bib70) 2021; 1 Angrisano (10.1016/j.measen.2022.100512_bib59) 2020; 8 Weiss (10.1016/j.measen.2022.100512_bib22) 2019 Chen (10.1016/j.measen.2022.100512_bib23) 2021; 54 Gani (10.1016/j.measen.2022.100512_bib37) 2009; 4 Neverova (10.1016/j.measen.2022.100512_bib62) 2016; 4 Johansson (10.1016/j.measen.2022.100512_bib26) 1973; 14 Elgethun (10.1016/j.measen.2022.100512_bib36) 2003; 111 Dewangan (10.1016/j.measen.2022.100512_bib40) 2021; 14 Panwar (10.1016/j.measen.2022.100512_bib43) 2017 Ignatov (10.1016/j.measen.2022.100512_bib15) 2018; 62 Seshadri (10.1016/j.measen.2022.100512_bib32) 2019; 2 Benegui (10.1016/j.measen.2022.100512_bib57) 2020; 8 Vrigkas (10.1016/j.measen.2022.100512_bib17) 2015; 2 Wang (10.1016/j.measen.2022.100512_bib67) 2021; 51 Dewangan (10.1016/j.measen.2022.100512_bib38) 2021; 57 Damirchi (10.1016/j.measen.2022.100512_bib69) 2020 Mozer (10.1016/j.measen.2022.100512_bib29) 1998; vol. 58 Li (10.1016/j.measen.2022.100512_bib54) 2021 Weiss (10.1016/j.measen.2022.100512_bib16) 2019; 7 Zhan (10.1016/j.measen.2022.100512_bib20) 2010; 130 Tapia (10.1016/j.measen.2022.100512_bib28) 2004 Musale (10.1016/j.measen.2022.100512_bib60) 2019; 7 Turaga (10.1016/j.measen.2022.100512_bib27) 2008; 18 Zhao (10.1016/j.measen.2022.100512_bib64) 2018; 2018 Corbett (10.1016/j.measen.2022.100512_bib33) 2001 Ullah (10.1016/j.measen.2022.100512_bib48) 2019 Zeng (10.1016/j.measen.2022.100512_bib47) 2014 Xi (10.1016/j.measen.2022.100512_bib41) 2020; 36 Hernández (10.1016/j.measen.2022.100512_bib49) 2019 Santos (10.1016/j.measen.2022.100512_bib18) 2019; 19 Wan (10.1016/j.measen.2022.100512_bib44) 2020; 25 Teng (10.1016/j.measen.2022.100512_bib46) 2020; 20 Pires (10.1016/j.measen.2022.100512_bib14) 2016; 16 Wang (10.1016/j.measen.2022.100512_bib1) 2019; 119 Mekruksavanich (10.1016/j.measen.2022.100512_bib56) 2021; 10 Weiss (10.1016/j.measen.2022.100512_bib58) 2019; 7 Parziale (10.1016/j.measen.2022.100512_bib55) 2021 Mutegeki (10.1016/j.measen.2022.100512_bib65) 2020 Aggarwal (10.1016/j.measen.2022.100512_bib25) 1999; 73 Dewangan (10.1016/j.measen.2022.100512_bib39) 2020; 21 Han (10.1016/j.measen.2022.100512_bib6) 2021; 17 Jobanputra (10.1016/j.measen.2022.100512_bib7) 2019; 155 Li (10.1016/j.measen.2022.100512_bib68) 2021 Dang (10.1016/j.measen.2022.100512_bib8) 2020; 108 Damaševičius (10.1016/j.measen.2022.100512_bib5) 2016; 8 Wang (10.1016/j.measen.2022.100512_bib45) 2019; 19 Mekruksavanich (10.1016/j.measen.2022.100512_bib19) 2020; 12 Mekruksavanich (10.1016/j.measen.2022.100512_bib11) 2021; 21 Lara (10.1016/j.measen.2022.100512_bib21) 2012; 15 |
| References_xml | – volume: 119 start-page: 3 year: 2019 end-page: 11 ident: bib1 article-title: Deep learning for sensor-based activity recognition: a survey publication-title: Pattern Recogn. Lett. – volume: 19 start-page: 7598 year: 2019 end-page: 7604 ident: bib45 article-title: Attention-based convolutional neural network for weakly labeled human activities' recognition with wearable sensors publication-title: IEEE Sensor. J. – volume: 15 start-page: 979 year: 1962 end-page: 984 ident: bib13 article-title: Multidisciplinary studies of illness in aged persons—VI: comparison study of rehabilitated and nonrehabilitated patients with fracture of the hip publication-title: J. Chron. Dis. – volume: 10 start-page: 308 year: 2021 ident: bib56 article-title: Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models publication-title: Electronics – volume: 19 start-page: 1644 year: 2019 ident: bib18 article-title: Accelerometer-based human fall detection using convolutional neural networks publication-title: Sensors – volume: 11 start-page: 259 year: 2019 ident: bib3 article-title: A review of internet of things technologies for ambient assisted living environments publication-title: Future Internet – start-page: 197 year: 2014, November end-page: 205 ident: bib47 article-title: Convolutional neural networks for human activity recognition using mobile sensors publication-title: 6th International Conference on – volume: 16 start-page: 115 year: 2016 ident: bib66 article-title: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition publication-title: Sensors – volume: 7 start-page: 37883 year: 2019 end-page: 37895 ident: bib60 article-title: You walk, we authenticate: lightweight seamless authentication based on gait in wearable IoT systems publication-title: IEEE Access – volume: 2 start-page: 1 year: 2019 end-page: 16 ident: bib32 article-title: Wearable sensors for monitoring the physiological and biochemical profile of the athlete publication-title: NPJ digital medicine – volume: 51 start-page: 355 year: 2021 end-page: 364 ident: bib53 article-title: Sequential weakly labeled multiactivity localization and recognition on wearable sensors using recurrent attention networks publication-title: IEEE Transact. Human-Machine Syst. – volume: 1 year: 2021 ident: bib70 article-title: Deep learning based human activity recognition (HAR) using wearable sensor data publication-title: International Journal of Information Management Data Insights – volume: 21 start-page: 1636 year: 2021 ident: bib11 article-title: Lstm networks using smartphone data for sensor-based human activity recognition in smart homes publication-title: Sensors – start-page: 16266 year: 2021 end-page: 16275 ident: bib68 article-title: Uav-human: a large benchmark for human behavior understanding with unmanned aerial vehicles publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 14 start-page: 201 year: 1973 end-page: 211 ident: bib26 article-title: Visual perception of biological motion and a model for its analysis publication-title: Percept. Psychophys. – volume: 57 start-page: 53 year: 2021 end-page: 56 ident: bib38 article-title: PotNet: pothole detection for autonomous vehicle system using convolutional neural network publication-title: Electron. Lett. – volume: 62 start-page: 915 year: 2018 end-page: 922 ident: bib15 article-title: Real-time human activity recognition from accelerometer data using Convolutional Neural Networks publication-title: Appl. Soft Comput. – volume: 25 start-page: 743 year: 2020 end-page: 755 ident: bib44 article-title: Deep learning models for real-time human activity recognition with smartphones publication-title: Mobile Network. Appl. – year: 2019 ident: bib22 article-title: UCI Machine Learning Repository – volume: 8 start-page: 27435 year: 2020 end-page: 27447 ident: bib59 article-title: Identification of walker identity using smartphone sensors: an experiment using ensemble learning publication-title: IEEE Access – volume: 2 start-page: 28 year: 2015 ident: bib17 article-title: A review of human activity recognition methods publication-title: Frontiers in Robotics and AI – volume: 1 start-page: 20 year: 2015 end-page: 29 ident: bib31 article-title: A review on radio based activity recognition publication-title: Digital Communicat. Networks – volume: 130 start-page: 953 year: 2018 end-page: 960 ident: bib4 article-title: Recognition of basketball referee signals from videos using histogram of oriented gradients (HOG) and support vector machine (SVM) publication-title: Procedia Comput. Sci. – volume: 130 start-page: 565 year: 2010 end-page: 572 ident: bib20 article-title: Human activity recognition from environmental background sounds for wireless sensor networks publication-title: IEEJ Transact. Electro. Informat. Systems – volume: 14 start-page: 199 year: 2021 end-page: 214 ident: bib40 article-title: RCNet: road classification convolutional neural networks for intelligent vehicle system publication-title: Intelligent Service Robotics – volume: 108 year: 2020 ident: bib8 article-title: Sensor-based and vision-based human activity recognition: a comprehensive survey publication-title: Pattern Recogn. – volume: 21 start-page: 3570 year: 2020 end-page: 3578 ident: bib39 article-title: Deep learning-based speed bump detection model for intelligent vehicle system using raspberry Pi publication-title: IEEE Sensor. J. – volume: 36 start-page: 1869 year: 2020 end-page: 1882 ident: bib41 article-title: An integrated approach for medical abnormality detection using deep patch convolutional neural networks publication-title: Vis. Comput. – start-page: 1 year: 2019, April end-page: 5 ident: bib49 article-title: Human activity recognition on smartphones using a bidirectional LSTM network publication-title: 2019 XXII Symposium on Image – volume: 18 start-page: 1473 year: 2008 end-page: 1488 ident: bib27 article-title: Machine recognition of human activities: a survey publication-title: IEEE Trans. Circ. Syst. Video Technol. – volume: 4 start-page: 1810 year: 2016 end-page: 1820 ident: bib62 article-title: Learning human identity from motion patterns publication-title: IEEE Access – start-page: 1472 year: 2015, April end-page: 1480 ident: bib30 article-title: Wigest: a ubiquitous wifi-based gesture recognition system publication-title: 2015 IEEE Conference on Computer Communications (INFOCOM) – volume: 17 start-page: 385 year: 2021 end-page: 398 ident: bib6 article-title: Residual learning based CNN for gesture recognition in robot interaction publication-title: J. Informat. Process. Syst. – volume: vol. 58 year: 1998, March ident: bib29 article-title: The neural network house: an environment hat adapts to its inhabitants publication-title: Proc. AAAI Spring Symp. Intelligent Environments – year: 2019 ident: bib24 article-title: Different Approaches for Human Activity Recognition: A Survey – volume: 7 start-page: 133190 year: 2019 end-page: 133202 ident: bib16 article-title: Smartphone and smartwatch-based biometrics using activities of daily living publication-title: IEEE Access – volume: 12 start-page: 1570 year: 2020 ident: bib19 article-title: Enhanced hand-oriented activity recognition based on smartwatch sensor data using lstms publication-title: Symmetry – start-page: 175 year: 2019, October end-page: 180 ident: bib63 article-title: Stacked lstm network for human activity recognition using smartphone data publication-title: 2019 8th European Workshop on – volume: 7 start-page: 133190 year: 2019 end-page: 133202 ident: bib58 article-title: Smartphone and smartwatch-based biometrics using activities of daily living publication-title: IEEE Access – volume: vol. 24 year: 1970 ident: bib35 publication-title: What about People in Regional Science – volume: 8 start-page: 100 year: 2016 ident: bib5 article-title: Smartphone user identity verification using gait characteristics publication-title: Symmetry – volume: 111 start-page: 115 year: 2003 end-page: 122 ident: bib36 article-title: Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument publication-title: Environ. Health Perspect. – volume: 2018 year: 2018 ident: bib50 article-title: Deep residual bidir-LSTM for human activity recognition using wearable sensors publication-title: Math. Probl Eng. – volume: 20 start-page: 7265 year: 2020 end-page: 7274 ident: bib46 article-title: The layer-wise training convolutional neural networks using local loss for sensor-based human activity recognition publication-title: IEEE Sensor. J. – volume: 2016 year: 2016 ident: bib2 article-title: Human activity recognition in AAL environments using random projections publication-title: Comput. Math. Methods Med. – year: 2001 ident: bib33 article-title: Torsten hӓgerstrand, time geography publication-title: CSISS Classics – start-page: 307 year: 2021, September end-page: 321 ident: bib55 article-title: 2D vs 3D online writer identification: a comparative study publication-title: International Conference on Docu – volume: 4 start-page: 976 year: 2009 end-page: 984 ident: bib37 article-title: Prediction of state of wireless network using markov and hidden markov model publication-title: J. Network. – start-page: 1 year: 2016, October end-page: 3 ident: bib10 article-title: Human activity recognition with inertial sensors using a deep learning approach publication-title: 2016 IEEE Sensors – start-page: 158 year: 2004, April end-page: 175 ident: bib28 article-title: Activity recognition in the home using simple and ubiquitous sensors publication-title: International Conference on – volume: 7 start-page: 275 year: 2003 end-page: 286 ident: bib34 article-title: Using GPS to learn significant locations and predict movement across multiple users publication-title: Personal Ubiquitous Comput. – start-page: 362 year: 2020, February end-page: 366 ident: bib51 article-title: A CNN-LSTM approach to human activity recognition publication-title: 2020 International Conference o – volume: 2018 year: 2018 ident: bib64 article-title: Deep residual bidir-LSTM for human activity recognition using wearable sensors publication-title: Math. Probl Eng. – volume: 51 start-page: 355 year: 2021 end-page: 364 ident: bib67 article-title: Sequential weakly labeled multiactivity localization and recognition on wearable sensors using recurrent attention networks publication-title: IEEE Transactions on Human-Machine Systems – volume: 10 start-page: 308 year: 2021 ident: bib12 article-title: Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models publication-title: Electronics – volume: 15 start-page: 1192 year: 2012 end-page: 1209 ident: bib21 article-title: A survey on human activity recognition using wearable sensors publication-title: IEEE Communicat. Surveys Tutorials – start-page: 175 year: 2019, October end-page: 180 ident: bib48 article-title: Stacked lstm network for human activity recognition using smartphone data publication-title: 2019 8th European Workshop on – start-page: 362 year: 2020, February end-page: 366 ident: bib65 article-title: A CNN-LSTM approach to human activity recognition publication-title: 2020 International Conference o – volume: 28 start-page: 565 year: 2017 end-page: 574 ident: bib42 article-title: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach publication-title: Neural Comput. Appl. – volume: 73 start-page: 428 year: 1999 end-page: 440 ident: bib25 article-title: Human motion analysis: a review publication-title: Comput. Vis. Image Understand. – volume: 155 start-page: 698 year: 2019 end-page: 703 ident: bib7 article-title: Human activity recognition: a survey publication-title: Procedia Comput. Sci. – year: 2018 ident: bib61 article-title: Smartwatch-based Legitimate User Identification for Cloud-Based Secure Services – volume: 54 start-page: 1 year: 2021 end-page: 40 ident: bib23 article-title: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities publication-title: ACM Comput. Surv. – volume: 16 start-page: 184 year: 2016 ident: bib14 article-title: From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices publication-title: Sensors – volume: 8 start-page: 56855 year: 2020 end-page: 56866 ident: bib52 article-title: LSTM-CNN architecture for human activity recognition publication-title: IEEE Access – start-page: 2438 year: 2017, July end-page: 2441 ident: bib43 article-title: CNN based approach for activity recognition using a wrist-worn accelerometer publication-title: 2017 39th Annual Internatio – start-page: 16266 year: 2021 end-page: 16275 ident: bib54 article-title: Uav-human: a large benchmark for human behavior understanding with unmanned aerial vehicles publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 8 start-page: 61255 year: 2020 end-page: 61266 ident: bib57 article-title: Convolutional neural networks for user identification based on motion sensors represented as images publication-title: IEEE Access – volume: 19 start-page: 9921 year: 2019 end-page: 9928 ident: bib9 article-title: CrossCount: a deep learning system for device-free human counting using WiFi publication-title: IEEE Sensor. J. – start-page: 1382 year: 2020, December end-page: 1388 ident: bib69 article-title: ARC-net: activity recognition through capsules publication-title: 2020 19th IEEE International Co – volume: 11 start-page: 259 issue: 12 year: 2019 ident: 10.1016/j.measen.2022.100512_bib3 article-title: A review of internet of things technologies for ambient assisted living environments publication-title: Future Internet doi: 10.3390/fi11120259 – start-page: 362 year: 2020 ident: 10.1016/j.measen.2022.100512_bib51 article-title: A CNN-LSTM approach to human activity recognition – start-page: 2438 year: 2017 ident: 10.1016/j.measen.2022.100512_bib43 article-title: CNN based approach for activity recognition using a wrist-worn accelerometer – volume: 2018 year: 2018 ident: 10.1016/j.measen.2022.100512_bib64 article-title: Deep residual bidir-LSTM for human activity recognition using wearable sensors publication-title: Math. Probl Eng. doi: 10.1155/2018/7316954 – volume: 19 start-page: 9921 issue: 21 year: 2019 ident: 10.1016/j.measen.2022.100512_bib9 article-title: CrossCount: a deep learning system for device-free human counting using WiFi publication-title: IEEE Sensor. J. doi: 10.1109/JSEN.2019.2928502 – volume: 1 start-page: 20 issue: 1 year: 2015 ident: 10.1016/j.measen.2022.100512_bib31 article-title: A review on radio based activity recognition publication-title: Digital Communicat. Networks doi: 10.1016/j.dcan.2015.02.006 – volume: 21 start-page: 3570 issue: 3 year: 2020 ident: 10.1016/j.measen.2022.100512_bib39 article-title: Deep learning-based speed bump detection model for intelligent vehicle system using raspberry Pi publication-title: IEEE Sensor. J. doi: 10.1109/JSEN.2020.3027097 – year: 2019 ident: 10.1016/j.measen.2022.100512_bib22 – volume: 14 start-page: 199 issue: 2 year: 2021 ident: 10.1016/j.measen.2022.100512_bib40 article-title: RCNet: road classification convolutional neural networks for intelligent vehicle system publication-title: Intelligent Service Robotics doi: 10.1007/s11370-020-00343-6 – start-page: 175 year: 2019 ident: 10.1016/j.measen.2022.100512_bib48 article-title: Stacked lstm network for human activity recognition using smartphone data – volume: vol. 24 year: 1970 ident: 10.1016/j.measen.2022.100512_bib35 – volume: 19 start-page: 7598 issue: 17 year: 2019 ident: 10.1016/j.measen.2022.100512_bib45 article-title: Attention-based convolutional neural network for weakly labeled human activities' recognition with wearable sensors publication-title: IEEE Sensor. J. doi: 10.1109/JSEN.2019.2917225 – volume: 8 start-page: 56855 year: 2020 ident: 10.1016/j.measen.2022.100512_bib52 article-title: LSTM-CNN architecture for human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2982225 – volume: 51 start-page: 355 issue: 4 year: 2021 ident: 10.1016/j.measen.2022.100512_bib53 article-title: Sequential weakly labeled multiactivity localization and recognition on wearable sensors using recurrent attention networks publication-title: IEEE Transact. Human-Machine Syst. doi: 10.1109/THMS.2021.3086008 – volume: 12 start-page: 1570 issue: 9 year: 2020 ident: 10.1016/j.measen.2022.100512_bib19 article-title: Enhanced hand-oriented activity recognition based on smartwatch sensor data using lstms publication-title: Symmetry doi: 10.3390/sym12091570 – start-page: 1 year: 2019 ident: 10.1016/j.measen.2022.100512_bib49 article-title: Human activity recognition on smartphones using a bidirectional LSTM network – volume: 7 start-page: 133190 year: 2019 ident: 10.1016/j.measen.2022.100512_bib58 article-title: Smartphone and smartwatch-based biometrics using activities of daily living publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2940729 – volume: 108 year: 2020 ident: 10.1016/j.measen.2022.100512_bib8 article-title: Sensor-based and vision-based human activity recognition: a comprehensive survey publication-title: Pattern Recogn. – volume: 28 start-page: 565 issue: 3 year: 2017 ident: 10.1016/j.measen.2022.100512_bib42 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: 2018 year: 2018 ident: 10.1016/j.measen.2022.100512_bib50 article-title: Deep residual bidir-LSTM for human activity recognition using wearable sensors publication-title: Math. Probl Eng. doi: 10.1155/2018/7316954 – volume: 8 start-page: 27435 year: 2020 ident: 10.1016/j.measen.2022.100512_bib59 article-title: Identification of walker identity using smartphone sensors: an experiment using ensemble learning publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2971693 – volume: 2016 year: 2016 ident: 10.1016/j.measen.2022.100512_bib2 article-title: Human activity recognition in AAL environments using random projections publication-title: Comput. Math. Methods Med. doi: 10.1155/2016/4073584 – start-page: 158 year: 2004 ident: 10.1016/j.measen.2022.100512_bib28 article-title: Activity recognition in the home using simple and ubiquitous sensors – volume: 57 start-page: 53 issue: 2 year: 2021 ident: 10.1016/j.measen.2022.100512_bib38 article-title: PotNet: pothole detection for autonomous vehicle system using convolutional neural network publication-title: Electron. Lett. doi: 10.1049/ell2.12062 – volume: 15 start-page: 1192 issue: 3 year: 2012 ident: 10.1016/j.measen.2022.100512_bib21 article-title: A survey on human activity recognition using wearable sensors publication-title: IEEE Communicat. Surveys Tutorials doi: 10.1109/SURV.2012.110112.00192 – volume: 130 start-page: 565 issue: 4 year: 2010 ident: 10.1016/j.measen.2022.100512_bib20 article-title: Human activity recognition from environmental background sounds for wireless sensor networks publication-title: IEEJ Transact. Electro. Informat. Systems – volume: 54 start-page: 1 issue: 4 year: 2021 ident: 10.1016/j.measen.2022.100512_bib23 article-title: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities publication-title: ACM Comput. Surv. – start-page: 16266 year: 2021 ident: 10.1016/j.measen.2022.100512_bib54 article-title: Uav-human: a large benchmark for human behavior understanding with unmanned aerial vehicles – volume: 7 start-page: 37883 year: 2019 ident: 10.1016/j.measen.2022.100512_bib60 article-title: You walk, we authenticate: lightweight seamless authentication based on gait in wearable IoT systems publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2906663 – volume: 7 start-page: 133190 year: 2019 ident: 10.1016/j.measen.2022.100512_bib16 article-title: Smartphone and smartwatch-based biometrics using activities of daily living publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2940729 – volume: 18 start-page: 1473 issue: 11 year: 2008 ident: 10.1016/j.measen.2022.100512_bib27 article-title: Machine recognition of human activities: a survey publication-title: IEEE Trans. Circ. Syst. Video Technol. doi: 10.1109/TCSVT.2008.2005594 – volume: 20 start-page: 7265 issue: 13 year: 2020 ident: 10.1016/j.measen.2022.100512_bib46 article-title: The layer-wise training convolutional neural networks using local loss for sensor-based human activity recognition publication-title: IEEE Sensor. J. doi: 10.1109/JSEN.2020.2978772 – volume: 16 start-page: 184 issue: 2 year: 2016 ident: 10.1016/j.measen.2022.100512_bib14 article-title: From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices publication-title: Sensors doi: 10.3390/s16020184 – volume: vol. 58 year: 1998 ident: 10.1016/j.measen.2022.100512_bib29 article-title: The neural network house: an environment hat adapts to its inhabitants – volume: 19 start-page: 1644 issue: 7 year: 2019 ident: 10.1016/j.measen.2022.100512_bib18 article-title: Accelerometer-based human fall detection using convolutional neural networks publication-title: Sensors doi: 10.3390/s19071644 – start-page: 1 year: 2016 ident: 10.1016/j.measen.2022.100512_bib10 article-title: Human activity recognition with inertial sensors using a deep learning approach – start-page: 1472 year: 2015 ident: 10.1016/j.measen.2022.100512_bib30 article-title: Wigest: a ubiquitous wifi-based gesture recognition system – volume: 4 start-page: 976 issue: 10 year: 2009 ident: 10.1016/j.measen.2022.100512_bib37 article-title: Prediction of state of wireless network using markov and hidden markov model publication-title: J. Network. doi: 10.4304/jnw.4.10.976-984 – volume: 17 start-page: 385 issue: 2 year: 2021 ident: 10.1016/j.measen.2022.100512_bib6 article-title: Residual learning based CNN for gesture recognition in robot interaction publication-title: J. Informat. Process. Syst. – volume: 8 start-page: 61255 year: 2020 ident: 10.1016/j.measen.2022.100512_bib57 article-title: Convolutional neural networks for user identification based on motion sensors represented as images publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2984214 – volume: 62 start-page: 915 year: 2018 ident: 10.1016/j.measen.2022.100512_bib15 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: 51 start-page: 355 issue: 4 year: 2021 ident: 10.1016/j.measen.2022.100512_bib67 article-title: Sequential weakly labeled multiactivity localization and recognition on wearable sensors using recurrent attention networks publication-title: IEEE Transactions on Human-Machine Systems doi: 10.1109/THMS.2021.3086008 – volume: 4 start-page: 1810 year: 2016 ident: 10.1016/j.measen.2022.100512_bib62 article-title: Learning human identity from motion patterns publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2557846 – start-page: 175 year: 2019 ident: 10.1016/j.measen.2022.100512_bib63 article-title: Stacked lstm network for human activity recognition using smartphone data – volume: 1 issue: 2 year: 2021 ident: 10.1016/j.measen.2022.100512_bib70 article-title: Deep learning based human activity recognition (HAR) using wearable sensor data publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2021.100046 – volume: 2 start-page: 28 year: 2015 ident: 10.1016/j.measen.2022.100512_bib17 article-title: A review of human activity recognition methods publication-title: Frontiers in Robotics and AI doi: 10.3389/frobt.2015.00028 – volume: 111 start-page: 115 issue: 1 year: 2003 ident: 10.1016/j.measen.2022.100512_bib36 article-title: Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument publication-title: Environ. Health Perspect. doi: 10.1289/ehp.5350 – volume: 25 start-page: 743 issue: 2 year: 2020 ident: 10.1016/j.measen.2022.100512_bib44 article-title: Deep learning models for real-time human activity recognition with smartphones publication-title: Mobile Network. Appl. doi: 10.1007/s11036-019-01445-x – volume: 36 start-page: 1869 issue: 9 year: 2020 ident: 10.1016/j.measen.2022.100512_bib41 article-title: An integrated approach for medical abnormality detection using deep patch convolutional neural networks publication-title: Vis. Comput. doi: 10.1007/s00371-019-01775-7 – start-page: 197 year: 2014 ident: 10.1016/j.measen.2022.100512_bib47 article-title: Convolutional neural networks for human activity recognition using mobile sensors – start-page: 307 year: 2021 ident: 10.1016/j.measen.2022.100512_bib55 article-title: 2D vs 3D online writer identification: a comparative study – year: 2019 ident: 10.1016/j.measen.2022.100512_bib24 – volume: 73 start-page: 428 issue: 3 year: 1999 ident: 10.1016/j.measen.2022.100512_bib25 article-title: Human motion analysis: a review publication-title: Comput. Vis. Image Understand. doi: 10.1006/cviu.1998.0744 – volume: 10 start-page: 308 issue: 3 year: 2021 ident: 10.1016/j.measen.2022.100512_bib56 article-title: Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models publication-title: Electronics doi: 10.3390/electronics10030308 – start-page: 1382 year: 2020 ident: 10.1016/j.measen.2022.100512_bib69 article-title: ARC-net: activity recognition through capsules – year: 2001 ident: 10.1016/j.measen.2022.100512_bib33 article-title: Torsten hӓgerstrand, time geography publication-title: CSISS Classics – volume: 8 start-page: 100 issue: 10 year: 2016 ident: 10.1016/j.measen.2022.100512_bib5 article-title: Smartphone user identity verification using gait characteristics publication-title: Symmetry doi: 10.3390/sym8100100 – volume: 7 start-page: 275 issue: 5 year: 2003 ident: 10.1016/j.measen.2022.100512_bib34 article-title: Using GPS to learn significant locations and predict movement across multiple users publication-title: Personal Ubiquitous Comput. doi: 10.1007/s00779-003-0240-0 – volume: 155 start-page: 698 year: 2019 ident: 10.1016/j.measen.2022.100512_bib7 article-title: Human activity recognition: a survey publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2019.08.100 – start-page: 362 year: 2020 ident: 10.1016/j.measen.2022.100512_bib65 article-title: A CNN-LSTM approach to human activity recognition – start-page: 16266 year: 2021 ident: 10.1016/j.measen.2022.100512_bib68 article-title: Uav-human: a large benchmark for human behavior understanding with unmanned aerial vehicles – volume: 2 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.measen.2022.100512_bib32 article-title: Wearable sensors for monitoring the physiological and biochemical profile of the athlete publication-title: NPJ digital medicine – volume: 15 start-page: 979 issue: 10 year: 1962 ident: 10.1016/j.measen.2022.100512_bib13 article-title: Multidisciplinary studies of illness in aged persons—VI: comparison study of rehabilitated and nonrehabilitated patients with fracture of the hip publication-title: J. Chron. Dis. doi: 10.1016/0021-9681(62)90117-0 – volume: 10 start-page: 308 issue: 3 year: 2021 ident: 10.1016/j.measen.2022.100512_bib12 article-title: Biometric user identification based on human activity recognition using wearable sensors: an experiment using deep learning models publication-title: Electronics doi: 10.3390/electronics10030308 – volume: 21 start-page: 1636 issue: 5 year: 2021 ident: 10.1016/j.measen.2022.100512_bib11 article-title: Lstm networks using smartphone data for sensor-based human activity recognition in smart homes publication-title: Sensors doi: 10.3390/s21051636 – volume: 130 start-page: 953 year: 2018 ident: 10.1016/j.measen.2022.100512_bib4 article-title: Recognition of basketball referee signals from videos using histogram of oriented gradients (HOG) and support vector machine (SVM) publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2018.04.095 – year: 2018 ident: 10.1016/j.measen.2022.100512_bib61 – volume: 119 start-page: 3 year: 2019 ident: 10.1016/j.measen.2022.100512_bib1 article-title: Deep learning for sensor-based activity recognition: a survey publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2018.02.010 – volume: 14 start-page: 201 issue: 2 year: 1973 ident: 10.1016/j.measen.2022.100512_bib26 article-title: Visual perception of biological motion and a model for its analysis publication-title: Percept. Psychophys. doi: 10.3758/BF03212378 – volume: 16 start-page: 115 issue: 1 year: 2016 ident: 10.1016/j.measen.2022.100512_bib66 article-title: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition publication-title: Sensors doi: 10.3390/s16010115 |
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