An unsupervised statistical representation learning method for human activity recognition
With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area...
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| Vydáno v: | Signal, image and video processing Ročník 18; číslo 10; s. 7041 - 7052 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer London
01.09.2024
Springer Nature B.V |
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| ISSN: | 1863-1703, 1863-1711 |
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| Abstract | With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area in the fields of ubiquitous computing and wearable computing. This paper presents a novel approach that employs Latent Dirichlet Allocation (LDA) to extract meaningful representations from activity signals. The method involves transforming the activity signal, which is a sequence of samples, into a sequence of discrete symbols using vector quantization. Subsequently, LDA is utilized to embed the symbol sequence into a fixed-length representation vector. Finally, a classifier is employed to classify the obtained representation vector. The effectiveness of the proposed method is evaluated using the UNIMIB-SHAR dataset. Experimental results demonstrate its competitive performance in terms of accuracy and F1-score metrics when compared to existing methods. Moreover, our method boasts a more lightweight architecture and incurs lower computational costs compared to deep learning-based approaches. The findings of this study contribute to the advancement of HAR and hold practical implications for HAR systems. |
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| AbstractList | With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area in the fields of ubiquitous computing and wearable computing. This paper presents a novel approach that employs Latent Dirichlet Allocation (LDA) to extract meaningful representations from activity signals. The method involves transforming the activity signal, which is a sequence of samples, into a sequence of discrete symbols using vector quantization. Subsequently, LDA is utilized to embed the symbol sequence into a fixed-length representation vector. Finally, a classifier is employed to classify the obtained representation vector. The effectiveness of the proposed method is evaluated using the UNIMIB-SHAR dataset. Experimental results demonstrate its competitive performance in terms of accuracy and F1-score metrics when compared to existing methods. Moreover, our method boasts a more lightweight architecture and incurs lower computational costs compared to deep learning-based approaches. The findings of this study contribute to the advancement of HAR and hold practical implications for HAR systems. |
| Author | Momeni, Saleh BabaAli, Bagher Abdi, Mohammad Foad |
| Author_xml | – sequence: 1 givenname: Mohammad Foad surname: Abdi fullname: Abdi, Mohammad Foad organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran – sequence: 2 givenname: Bagher surname: BabaAli fullname: BabaAli, Bagher email: babaali@ut.ac.ir organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran – sequence: 3 givenname: Saleh surname: Momeni fullname: Momeni, Saleh organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran |
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| Cites_doi | 10.1109/SURV.2012.110112.00192 10.1145/3090076 10.1145/3551486 10.1109/JSEN.2022.3149337 10.1145/3596234 10.1145/2499621 10.1109/TBME.2014.2307069 10.1016/j.asoc.2021.107728 10.1016/j.pmcj.2016.09.009 10.1109/TSMCC.2012.2198883 10.1007/s11548-021-02493-z 10.3390/app7101101 10.3390/s16010115 10.1007/s11042-018-6894-4 10.3390/s22041476 10.1109/JSEN.2020.3015521 10.1109/TIE.2022.3161812 10.1109/83.541428 10.1016/j.neucom.2015.07.085 10.1016/j.patrec.2018.02.010 10.1145/1964897.1964918 10.1109/34.954607 10.1145/3517246 10.1109/TETCI.2021.3136642 10.1109/ACCESS.2020.3037715 10.1016/j.patcog.2007.04.015 10.1109/ICIEV.2016.7760005 10.1145/3267242.3267287 10.1109/WACV.2013.6474999 10.1145/2968219.2971416 10.1007/978-3-642-35395-6_30 10.24963/ijcai.2019/779 10.1145/3460421.3480419 10.3389/fnins.2023.1233037 |
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| Keywords | Feature embedding Latent Dirichlet allocation (LDA) Human activity recognition Unsupervised representation learning |
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| References | Blei, Ng, Jordan (CR9) 2003; 3 Paysan, Haug, Bajka, Oelhafen, Buhmann (CR6) 2021; 16 Bulling, Blanke, Schiele (CR1) 2014; 46 Shuai Shao, Guan, Missier, Plötz (CR31) 2023; 7 CR18 CR16 CR11 CR33 Chen, Hoey, Nugent, Cook, Zhiwen (CR12) 2012; 42 Tang, Zhang, Min, He (CR44) 2022; 70 CR30 Gersho, Gray (CR34) 2012 Gao, Zhang, Teng, He, Hao (CR40) 2021; 111 Lai, Liaw, Liu (CR35) 2008; 41 Noor, Salcic, Kevin, Wang (CR19) 2017; 38 Ordóñez, Roggen (CR37) 2016; 16 Micucci, Mobilio, Napoletano (CR10) 2017; 7 Tang, Zhang, Teng, Min, Song (CR41) 2022; 6 Hsieh, Tsai (CR36) 1996; 5 Chen, Zhang, Yao, Guo, Zhiwen, Liu (CR13) 2021; 54 Kwapisz, Weiss, Moore (CR15) 2011; 12 Huang, Zhang, Wang, Hao, Song (CR42) 2022; 22 CR4 Cheng, Zhang, Tang, Liu, Hao, He (CR7) 2022; 22 CR29 CR28 CR26 Reyes-Ortiz, Oneto, Samà, Parra, Anguita (CR17) 2016; 171 CR24 CR23 Jelodar, Wang, Yuan, Feng, Jiang, Li, Zhao (CR38) 2019; 78 CR21 CR43 Ordóñez, Roggen (CR25) 2016; 16 Wang, Chen, Hao, Peng, Lisha (CR14) 2019; 119 Picard, Vyzas, Healey (CR22) 2001; 23 Lara, Labrador (CR3) 2012; 15 Jain, Tang, Min, Kawsar, Mathur (CR5) 2022; 6 Tang, Teng, Zhang, Min, He (CR39) 2020; 21 Gupta, Dallas (CR20) 2014; 61 Demrozi, Pravadelli, Bihorac, Rashidi (CR2) 2020; 8 Rong, Chen, Miao, Tang (CR32) 2023; 37 Zhang, Li, Zhang, Shahabi, Xia, Deng, Alshurafa (CR8) 2022; 22 Guan, Plötz (CR27) 2017; 1 J Wang (3374_CR14) 2019; 119 3374_CR30 A Gersho (3374_CR34) 2012 Y Tang (3374_CR41) 2022; 6 MHM Noor (3374_CR19) 2017; 38 RW Picard (3374_CR22) 2001; 23 OD Lara (3374_CR3) 2012; 15 FJ Ordóñez (3374_CR25) 2016; 16 C-H Hsieh (3374_CR36) 1996; 5 D Micucci (3374_CR10) 2017; 7 Yu Guan (3374_CR27) 2017; 1 W Huang (3374_CR42) 2022; 22 3374_CR18 F Demrozi (3374_CR2) 2020; 8 A Bulling (3374_CR1) 2014; 46 W Gao (3374_CR40) 2021; 111 D Paysan (3374_CR6) 2021; 16 K Chen (3374_CR13) 2021; 54 3374_CR11 3374_CR33 3374_CR16 L Chen (3374_CR12) 2012; 42 Y Tang (3374_CR44) 2022; 70 DM Blei (3374_CR9) 2003; 3 Y Tang (3374_CR39) 2020; 21 X Cheng (3374_CR7) 2022; 22 FJ Ordóñez (3374_CR37) 2016; 16 H Rong (3374_CR32) 2023; 37 Yu Shuai Shao (3374_CR31) 2023; 7 H Jelodar (3374_CR38) 2019; 78 3374_CR29 Y Jain (3374_CR5) 2022; 6 J-L Reyes-Ortiz (3374_CR17) 2016; 171 3374_CR24 S Zhang (3374_CR8) 2022; 22 3374_CR23 P Gupta (3374_CR20) 2014; 61 3374_CR21 JZC Lai (3374_CR35) 2008; 41 3374_CR43 JR Kwapisz (3374_CR15) 2011; 12 3374_CR28 3374_CR4 3374_CR26 |
| References_xml | – volume: 15 start-page: 1192 issue: 3 year: 2012 end-page: 1209 ident: CR3 article-title: A survey on human activity recognition using wearable sensors publication-title: IEEE Commun. Surveys Tutor. doi: 10.1109/SURV.2012.110112.00192 – volume: 1 start-page: 1 issue: 2 year: 2017 end-page: 28 ident: CR27 article-title: Ensembles of deep LSTM learners for activity recognition using wearables publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3090076 – ident: CR18 – ident: CR43 – year: 2012 ident: CR34 publication-title: Vector Quantization and Signal Compression – volume: 22 start-page: 1 issue: 1 year: 2022 end-page: 23 ident: CR42 article-title: Deep ensemble learning for human activity recognition using wearable sensors via filter activation publication-title: ACM Trans. Embed. Comput. Syst. doi: 10.1145/3551486 – ident: CR4 – volume: 22 start-page: 5889 issue: 6 year: 2022 end-page: 5901 ident: CR7 article-title: Real-time human activity recognition using conditionally parametrized convolutions on mobile and wearable devices publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3149337 – ident: CR16 – volume: 7 start-page: 1 issue: 2 year: 2023 end-page: 21 ident: CR31 article-title: Convboost: boosting convnets for sensor-based activity recognition publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3596234 – ident: CR30 – volume: 46 start-page: 1 issue: 3 year: 2014 end-page: 33 ident: CR1 article-title: A tutorial on human activity recognition using body-worn inertial sensors publication-title: ACM Comput. Surveys (CSUR) doi: 10.1145/2499621 – volume: 61 start-page: 1780 issue: 6 year: 2014 end-page: 1786 ident: CR20 article-title: Feature selection and activity recognition system using a single triaxial accelerometer publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2307069 – ident: CR33 – volume: 3 start-page: 993 issue: Jan year: 2003 end-page: 1022 ident: CR9 article-title: Latent Dirichlet allocation publication-title: J. Mach. Learn. Res. – volume: 111 start-page: 107728 year: 2021 ident: CR40 article-title: DanHAR: dual attention network for multimodal human activity recognition using wearable sensors publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107728 – volume: 38 start-page: 41 year: 2017 end-page: 59 ident: CR19 article-title: Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer publication-title: Pervasive Mobile Comput. doi: 10.1016/j.pmcj.2016.09.009 – ident: CR29 – volume: 42 start-page: 790 issue: 6 year: 2012 end-page: 808 ident: CR12 article-title: Sensor-based activity recognition publication-title: IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) doi: 10.1109/TSMCC.2012.2198883 – volume: 54 start-page: 1 issue: 4 year: 2021 end-page: 40 ident: CR13 article-title: Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities publication-title: ACM Comput. Surveys (CSUR) – volume: 16 start-page: 2037 year: 2021 end-page: 2044 ident: CR6 article-title: Self-supervised representation learning for surgical activity recognition publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-021-02493-z – ident: CR23 – volume: 7 start-page: 1101 issue: 10 year: 2017 ident: CR10 article-title: Unimib shar: a dataset for human activity recognition using acceleration data from smartphones publication-title: Appl. Sci. doi: 10.3390/app7101101 – ident: CR21 – volume: 16 start-page: 115 issue: 1 year: 2016 ident: CR37 article-title: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition publication-title: Sensors doi: 10.3390/s16010115 – volume: 78 start-page: 15169 year: 2019 end-page: 15211 ident: CR38 article-title: Latent Dirichlet Allocation (LDA) and topic modeling: models, applications, a survey publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-018-6894-4 – volume: 22 start-page: 1476 issue: 4 year: 2022 ident: CR8 article-title: Deep learning in human activity recognition with wearable sensors: a review on advances publication-title: Sensors doi: 10.3390/s22041476 – volume: 21 start-page: 581 issue: 1 year: 2020 end-page: 592 ident: CR39 article-title: Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.3015521 – volume: 70 start-page: 2106 issue: 2 year: 2022 end-page: 2116 ident: CR44 article-title: Multiscale deep feature learning for human activity recognition using wearable sensors publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2022.3161812 – volume: 5 start-page: 1579 issue: 11 year: 1996 end-page: 1582 ident: CR36 article-title: Lossless compression of VQ index with search-order coding publication-title: IEEE Trans. Image Process. doi: 10.1109/83.541428 – ident: CR11 – volume: 171 start-page: 754 year: 2016 end-page: 767 ident: CR17 article-title: Transition-aware human activity recognition using smartphones publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.07.085 – volume: 119 start-page: 3 year: 2019 end-page: 11 ident: CR14 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: 12 start-page: 74 issue: 2 year: 2011 end-page: 82 ident: CR15 article-title: Activity recognition using cell phone accelerometers publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1964897.1964918 – volume: 23 start-page: 1175 issue: 10 year: 2001 end-page: 1191 ident: CR22 article-title: Toward machine emotional intelligence: analysis of affective physiological state publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.954607 – volume: 6 start-page: 1 issue: 1 year: 2022 end-page: 28 ident: CR5 article-title: Collossl: collaborative self-supervised learning for human activity recognition publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3517246 – ident: CR28 – volume: 6 start-page: 1167 issue: 5 year: 2022 end-page: 1176 ident: CR41 article-title: Triple cross-domain attention on human activity recognition using wearable sensors publication-title: IEEE Trans. Emerg. Topics Comput. Intell. doi: 10.1109/TETCI.2021.3136642 – volume: 8 start-page: 210816 year: 2020 end-page: 210836 ident: CR2 article-title: Human activity recognition using inertial, physiological and environmental sensors: a comprehensive survey publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3037715 – ident: CR26 – ident: CR24 – volume: 37 start-page: 6012 year: 2023 end-page: 6020 ident: CR32 article-title: Swl-adapt: an unsupervised domain adaptation model with sample weight learning for cross-user wearable human activity recognition publication-title: Proc. AAAI Conf. Artif. Intell. – volume: 41 start-page: 315 issue: 1 year: 2008 end-page: 319 ident: CR35 article-title: A fast VQ codebook generation algorithm using codeword displacement publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2007.04.015 – volume: 16 start-page: 115 issue: 1 year: 2016 ident: CR25 article-title: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition publication-title: Sensors doi: 10.3390/s16010115 – ident: 3374_CR18 doi: 10.1109/ICIEV.2016.7760005 – volume: 3 start-page: 993 issue: Jan year: 2003 ident: 3374_CR9 publication-title: J. Mach. Learn. Res. – volume-title: Vector Quantization and Signal Compression year: 2012 ident: 3374_CR34 – volume: 78 start-page: 15169 year: 2019 ident: 3374_CR38 publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-018-6894-4 – ident: 3374_CR28 doi: 10.1145/3267242.3267287 – volume: 37 start-page: 6012 year: 2023 ident: 3374_CR32 publication-title: Proc. AAAI Conf. Artif. Intell. – volume: 1 start-page: 1 issue: 2 year: 2017 ident: 3374_CR27 publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3090076 – ident: 3374_CR23 doi: 10.1109/WACV.2013.6474999 – volume: 8 start-page: 210816 year: 2020 ident: 3374_CR2 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3037715 – ident: 3374_CR21 doi: 10.1145/2968219.2971416 – ident: 3374_CR33 – ident: 3374_CR16 doi: 10.1007/978-3-642-35395-6_30 – ident: 3374_CR29 doi: 10.24963/ijcai.2019/779 – volume: 22 start-page: 1476 issue: 4 year: 2022 ident: 3374_CR8 publication-title: Sensors doi: 10.3390/s22041476 – volume: 42 start-page: 790 issue: 6 year: 2012 ident: 3374_CR12 publication-title: IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) doi: 10.1109/TSMCC.2012.2198883 – volume: 16 start-page: 115 issue: 1 year: 2016 ident: 3374_CR25 publication-title: Sensors doi: 10.3390/s16010115 – volume: 16 start-page: 2037 year: 2021 ident: 3374_CR6 publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-021-02493-z – volume: 16 start-page: 115 issue: 1 year: 2016 ident: 3374_CR37 publication-title: Sensors doi: 10.3390/s16010115 – ident: 3374_CR26 – ident: 3374_CR30 doi: 10.1145/3460421.3480419 – volume: 21 start-page: 581 issue: 1 year: 2020 ident: 3374_CR39 publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.3015521 – volume: 12 start-page: 74 issue: 2 year: 2011 ident: 3374_CR15 publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/1964897.1964918 – ident: 3374_CR24 – volume: 22 start-page: 1 issue: 1 year: 2022 ident: 3374_CR42 publication-title: ACM Trans. Embed. Comput. Syst. doi: 10.1145/3551486 – volume: 6 start-page: 1167 issue: 5 year: 2022 ident: 3374_CR41 publication-title: IEEE Trans. Emerg. Topics Comput. Intell. doi: 10.1109/TETCI.2021.3136642 – volume: 7 start-page: 1 issue: 2 year: 2023 ident: 3374_CR31 publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3596234 – ident: 3374_CR4 – volume: 41 start-page: 315 issue: 1 year: 2008 ident: 3374_CR35 publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2007.04.015 – ident: 3374_CR11 – volume: 54 start-page: 1 issue: 4 year: 2021 ident: 3374_CR13 publication-title: ACM Comput. Surveys (CSUR) – volume: 15 start-page: 1192 issue: 3 year: 2012 ident: 3374_CR3 publication-title: IEEE Commun. Surveys Tutor. doi: 10.1109/SURV.2012.110112.00192 – volume: 6 start-page: 1 issue: 1 year: 2022 ident: 3374_CR5 publication-title: Proc. ACM Interact. Mobile Wear. Ubiq. Technol. doi: 10.1145/3517246 – volume: 171 start-page: 754 year: 2016 ident: 3374_CR17 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.07.085 – volume: 23 start-page: 1175 issue: 10 year: 2001 ident: 3374_CR22 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.954607 – volume: 22 start-page: 5889 issue: 6 year: 2022 ident: 3374_CR7 publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3149337 – volume: 7 start-page: 1101 issue: 10 year: 2017 ident: 3374_CR10 publication-title: Appl. Sci. doi: 10.3390/app7101101 – volume: 111 start-page: 107728 year: 2021 ident: 3374_CR40 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107728 – volume: 70 start-page: 2106 issue: 2 year: 2022 ident: 3374_CR44 publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2022.3161812 – ident: 3374_CR43 doi: 10.3389/fnins.2023.1233037 – volume: 119 start-page: 3 year: 2019 ident: 3374_CR14 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2018.02.010 – volume: 5 start-page: 1579 issue: 11 year: 1996 ident: 3374_CR36 publication-title: IEEE Trans. Image Process. doi: 10.1109/83.541428 – volume: 46 start-page: 1 issue: 3 year: 2014 ident: 3374_CR1 publication-title: ACM Comput. Surveys (CSUR) doi: 10.1145/2499621 – volume: 61 start-page: 1780 issue: 6 year: 2014 ident: 3374_CR20 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2307069 – volume: 38 start-page: 41 year: 2017 ident: 3374_CR19 publication-title: Pervasive Mobile Comput. doi: 10.1016/j.pmcj.2016.09.009 |
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