{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:59:56Z","timestamp":1781884796706,"version":"3.54.5"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Robot Syst"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10846-023-01926-y","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T09:02:36Z","timestamp":1692262956000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatiotemporal Self-Attention Mechanism Driven by 3D Pose to Guide RGB Cues for Daily Living Human Activity Recognition"],"prefix":"10.1007","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2632-8544","authenticated-orcid":false,"given":"Hend","family":"Basly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Amine","family":"Zayene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma Ezahra","family":"Sayadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"1926_CR1","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6299\u20136308 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"1926_CR2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1926_CR3","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"1926_CR4","doi-asserted-by":"crossref","unstructured":"Donahue, J., Hendricks, A. L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2625\u20132634 (2015)","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"1926_CR5","doi-asserted-by":"crossref","unstructured":"Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, 4041\u20134049 (2015)","DOI":"10.1109\/ICCV.2015.460"},{"key":"1926_CR6","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Hong, G.S., Kim, B.G., Dogra, D.P.: deepgesture: Deep learning-based gesture recognition scheme using motion sensors. Displays 55, 38\u201345","DOI":"10.1016\/j.displa.2018.08.001"},{"key":"1926_CR7","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization arXiv:1409.2329"},{"key":"1926_CR8","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, ... A.\u00a0N., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process Syst. 30 (2017)"},{"key":"1926_CR9","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.neuroimage.2015.09.051","volume":"124","author":"F Faugeras","year":"2016","unstructured":"Faugeras, F., Naccache, L.: Dissociating temporal attention from spatial attention and motor response preparation: a high-density eeg study. NeuroImage 124, 947\u2013957 (2016)","journal-title":"NeuroImage"},{"key":"1926_CR10","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.inffus.2021.11.006","volume":"80","author":"S Qiu","year":"2022","unstructured":"Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., Fortino, G.: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Inf. Fusion 80, 241\u2013265 (2022)","journal-title":"Inf. Fusion"},{"key":"1926_CR11","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.inffus.2022.10.015","volume":"91","author":"Y Li","year":"2023","unstructured":"Li, Y., Yang, G., Su, Z., Li, S., Wang, Y.: Human activity recognition based on multienvironment sensor data. Inf. Fusion. 91, 47\u201363 (2023)","journal-title":"Inf. Fusion."},{"key":"1926_CR12","doi-asserted-by":"crossref","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, 3551\u20133558 (2013)","DOI":"10.1109\/ICCV.2013.441"},{"key":"1926_CR13","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 31, 3551\u20133558 (2017)"},{"key":"1926_CR14","unstructured":"Ch\u00e9ron, G., Laptev, I., Schmid, C.: P-cnn: Pose-based cnn features for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, 218\u20133226 (2017)"},{"key":"1926_CR15","doi-asserted-by":"crossref","unstructured":"Basly, H., Ouarda, W., Sayadi, F.E., Ouni, B., Alimi, A.M.: Cnn-svm learning approach based human activity recognition. In: Proceedings of the International Conference on Image and Signal Processing, Springer, 271\u2013281 (2020)","DOI":"10.1007\/978-3-030-51935-3_29"},{"issue":"4","key":"1926_CR16","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1504\/IJBM.2021.117855","volume":"13","author":"H Basly","year":"2021","unstructured":"Basly, H., Ouarda, W., Sayadi, F.E., Ouni, B., Alimi, A.M.: Lahar-cnn: human activity recognition from one image using convolutional neural network learning approach. Int J Biomet 13(4), 385\u2013408 (2021)","journal-title":"Int J Biomet"},{"issue":"3","key":"1926_CR17","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1007\/s00371-021-02064-y","volume":"38","author":"H Basly","year":"2022","unstructured":"Basly, H., Ouarda, W., Sayadi, F.E., Ouni, B., Alimi, A.M.: Dtr-har: deep temporal residual representation for human activity recognition. Vis Comput 38(3), 993\u20131013 (2022)","journal-title":"Vis Comput"},{"key":"1926_CR18","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Adv. Neural Inf. Process. Syst. 27 (2014)"},{"issue":"1","key":"1926_CR19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"53","author":"S Ji","year":"2012","unstructured":"Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 53(1), 221\u2013231 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1926_CR20","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"1926_CR21","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248\u2013255 (2009). IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1926_CR22","doi-asserted-by":"crossref","unstructured":"Sigurdsson, G.A., Russakovsky, O., Gupta, A.: What actions are needed for understanding human actions in videos? In: Proceedings of the IEEE International Conference on Computer Vision, 2137\u20132146 (2017)","DOI":"10.1109\/ICCV.2017.235"},{"key":"1926_CR23","unstructured":"Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., ..., Zisserman, A.: The kinetics human action video dataset. arXiv:1705.06950"},{"key":"1926_CR24","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks arXiv:1609.02907"},{"key":"1926_CR25","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"1926_CR26","unstructured":"Chaolong, L., Zhen, C., Wenming, Z., Chunyan, X., Jian, Y.: Spatio-temporal graph convolution for skeleton based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)"},{"key":"1926_CR27","doi-asserted-by":"crossref","unstructured":"Li, B., Li, X., Zhang, Z., Wu, F.: Spatio-temporal graph routing for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 8561\u20138568 (2019)","DOI":"10.1609\/aaai.v33i01.33018561"},{"key":"1926_CR28","doi-asserted-by":"crossref","unstructured":"Gao, X., Hu, W., Tang, J., Liu, J., Guo, Z.: Optimized skeleton-based action recognition via sparsified graph regression. In: Proceedings of the 27th ACM International Conference on Multimedia, 601\u2013610 (2019)","DOI":"10.1145\/3343031.3351170"},{"key":"1926_CR29","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"1926_CR30","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, Y., Zhang, X., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3595\u20133603 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"key":"1926_CR31","doi-asserted-by":"crossref","unstructured":"Tang, Y., Tian, Y., Lu, J., Li, P., Zhou, J.: Deep progressive reinforcement learning for skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5323\u20135332 (2018)","DOI":"10.1109\/CVPR.2018.00558"},{"key":"1926_CR32","doi-asserted-by":"crossref","unstructured":"Peng, W., Hong, X., Chen, H., Zhao, G.: Learning graph convolutional network for skeleton-based human action recognition by neural searching. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2669\u20132676 (2020)","DOI":"10.1609\/aaai.v34i03.5652"},{"key":"1926_CR33","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning (2016) arXiv:1611.01578"},{"key":"1926_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104552","volume":"82","author":"G Yang","year":"2023","unstructured":"Yang, G., Liu, S., Li, Y., He, L.: Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients. Biomed. Signal Process. Control 82, 104552 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"1926_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104206","volume":"79","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Yang, G., Li, S., Li, Y., He, L., Liu, D.: Arrhythmia classification algorithm based on multi-head self-attention mechanism. Biomed. Signal Process. Control 79, 104206 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"1926_CR36","doi-asserted-by":"crossref","unstructured":"Song, S., Lan, C., Xing, J., Zeng, W., Liu, J.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"1926_CR37","unstructured":"Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention arXiv:1511.04119 (2015)"},{"key":"1926_CR38","unstructured":"Girdhar, R., Ramanan, D.: Attentional pooling for action recognition. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"1926_CR39","doi-asserted-by":"crossref","unstructured":"Long, X., Gan, C., De\u00a0Melo, G., Wu, J., Liu, X., Wen, S.: Attention clusters: Purely attention based local feature integration for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7834\u20137843 (2018)","DOI":"10.1109\/CVPR.2018.00817"},{"key":"1926_CR40","doi-asserted-by":"crossref","unstructured":"Baradel, F., Wolf, C., Mille, J., Taylor, G.W.: Glimpse clouds: Human activity recognition from unstructured feature points. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 469\u2013478 (2018)","DOI":"10.1109\/CVPR.2018.00056"},{"issue":"5","key":"1926_CR41","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNNLS.2019.2927224","volume":"31","author":"K Chen","year":"2019","unstructured":"Chen, K., Yao, L., Zhang, D., Wang, X., Chang, X., Nie, F.: A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1747\u20131756 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1926_CR42","doi-asserted-by":"crossref","unstructured":"Araei, S., Nadian-Ghomsheh, A.: Spatio-temporal 3d action recognition with hierarchical self-attention mechanism. In: 26th International Computer Conference, Computer Society of Iran (CSICC), 1\u20135 (2021). IEEE","DOI":"10.1109\/CSICC52343.2021.9420631"},{"key":"1926_CR43","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1926_CR44","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 244\u2013253 (2019)","DOI":"10.1109\/CVPR.2019.00033"},{"key":"1926_CR45","doi-asserted-by":"crossref","unstructured":"Plizzari, C., Cannici, M., Matteucci, M.: Spatial temporal transformer network for skeleton-based action recognition. In: International Conference on Pattern Recognition, Springer 694\u2013701 (2021)","DOI":"10.1007\/978-3-030-68796-0_50"},{"key":"1926_CR46","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, PMLR 448\u2013456 (2015)"},{"key":"1926_CR47","unstructured":"Nguyen, T.Q., Salazar, J.: Transformers without tears: Improving the normalization of self-attention arXiv:1910.05895 (2019)"},{"issue":"17","key":"1926_CR48","doi-asserted-by":"publisher","first-page":"19157","DOI":"10.1109\/JSEN.2021.3089705","volume":"21","author":"X Weiyao","year":"2021","unstructured":"Weiyao, X., Muqing, W., Min, Z., Ting, X.: Fusion of skeleton and rgb features for rgb-d human action recognition. IEEE Sens J 21(17), 19157\u201319164 (2021)","journal-title":"IEEE Sens J"},{"key":"1926_CR49","unstructured":"Joze, H.R.V., Shaban, A., Iuzzolino, M.L., Koishida, K.: Mmtm: Multimodal transfer module for cnn fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 13289\u201313299 (2020)"},{"key":"1926_CR50","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization arXiv:1412.6980 (2014)"},{"key":"1926_CR51","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7912\u20137921 (2019)","DOI":"10.1109\/CVPR.2019.00810"},{"key":"1926_CR52","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6546\u20136555 (2018)","DOI":"10.1109\/CVPR.2018.00685"},{"key":"1926_CR53","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 143\u2013152 (2020)","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"1926_CR54","doi-asserted-by":"crossref","unstructured":"Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding lstm networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1012\u20131020 (2017)","DOI":"10.1109\/ICCV.2017.115"},{"key":"1926_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE International Conference on Computer Vision, 2117\u20132126 (2017)","DOI":"10.1109\/ICCV.2017.233"},{"key":"1926_CR56","doi-asserted-by":"crossref","unstructured":"Baradel, F., Wolf, C., Mille, J.: Human action recognition: Pose-based attention draws focus to hands. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 604\u2013613 (2017)","DOI":"10.1109\/ICCVW.2017.77"},{"key":"1926_CR57","unstructured":"Baradel, F., Wolf, C., Mille, J.: Human activity recognition with pose-driven attention to rgb. In: BMVC 2018-29th British Machine Vision Conference, 1\u201314 (2018)"},{"key":"1926_CR58","doi-asserted-by":"crossref","unstructured":"Liu, G., Qian, J., Wen, F., Zhu, X., Ying, R., Liu, P.: Action recognition based on 3d skeleton and rgb frame fusion. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE 258\u2013264 (2019)","DOI":"10.1109\/IROS40897.2019.8967570"},{"key":"1926_CR59","doi-asserted-by":"crossref","unstructured":"Baradel, C., Wolf, F., Mille, J., Taylor, G.W.: Glimpse clouds: Human activity recognition from unstructured feature points. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 469\u2013478 (2018)","DOI":"10.1109\/CVPR.2018.00056"},{"key":"1926_CR60","unstructured":"Shi, F., Lee, C., Qiu, L., Zhao, Y., Shen, T., Muralidhar, S...., Narayanan, V.: Star: Sparse transformer-based action recognition arXiv:2107.07089 (2021)"},{"key":"1926_CR61","doi-asserted-by":"crossref","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation arXiv:1804.06055 (2018)","DOI":"10.24963\/ijcai.2018\/109"},{"key":"1926_CR62","doi-asserted-by":"crossref","unstructured":"Cho, S., Maqbool, M., Liu, F., Foroosh, H.: Self-attention network for skeleton-based human action recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 635\u2013644 (2020)","DOI":"10.1109\/WACV45572.2020.9093639"},{"issue":"16","key":"1926_CR63","doi-asserted-by":"publisher","first-page":"5339","DOI":"10.3390\/s21165339","volume":"21","author":"Y Sun","year":"2021","unstructured":"Sun, Y., Shen, Y., Ma, L.: Msst-rt: Multi-stream spatial-temporal relative transformer for skeleton-based action recognition. Sensors 21(16), 5339 (2021)","journal-title":"Sensors"},{"key":"1926_CR64","doi-asserted-by":"publisher","first-page":"228108","DOI":"10.1109\/ACCESS.2020.3046142","volume":"8","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Wang, Z., Zhuang, S., Huang, F.: Structure-feature fusion adaptive graph convolutional networks for skeleton-based action recognition. IEEE Access 8, 228108\u2013228117 (2020)","journal-title":"IEEE Access"},{"key":"1926_CR65","doi-asserted-by":"crossref","unstructured":"Liu, M., Yuan, J.: Recognizing human actions as the evolution of pose estimation maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1159\u20131168 (2018)","DOI":"10.1109\/CVPR.2018.00127"},{"key":"1926_CR66","doi-asserted-by":"crossref","unstructured":"Das, S., Dai, R., Koperski, M., Minciullo, L., Garattoni, L., Bremond, F., Francesca, G.: Toyota smarthome: Real-world activities of daily living. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 833\u2013842 (2019)","DOI":"10.1109\/ICCV.2019.00092"}],"container-title":["Journal of Intelligent & Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-023-01926-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10846-023-01926-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10846-023-01926-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T14:39:36Z","timestamp":1695998376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10846-023-01926-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1926"],"URL":"https:\/\/doi.org\/10.1007\/s10846-023-01926-y","relation":{},"ISSN":["0921-0296","1573-0409"],"issn-type":[{"value":"0921-0296","type":"print"},{"value":"1573-0409","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"assertion":[{"value":"27 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The participant has consented to the submission of the research manuscript to the journal.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"2"}}