{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:57:16Z","timestamp":1772823436377,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J01566"],"award-info":[{"award-number":["2022J01566"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s13042-023-02015-0","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T10:02:56Z","timestamp":1700301776000},"page":"2049-2062","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Skeleton-based 3D human pose estimation with low-resolution infrared array sensor using attention based CNN-BiGRU"],"prefix":"10.1007","volume":"15","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6902-9245","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiren","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunyi","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"issue":"4","key":"2015_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3390\/technologies6040089","volume":"6","author":"F Gr\u00fctzmacher","year":"2018","unstructured":"Gr\u00fctzmacher F, Hein A, Kirste T, Haubelt C (2018) Model-based design of energy-efficient human activity recognition systems with wearable sensors. Technologies 6(4):89. https:\/\/doi.org\/10.3390\/technologies6040089","journal-title":"Technologies"},{"key":"2015_CR2","doi-asserted-by":"crossref","unstructured":"I.\u00a0Khokhlov, L.\u00a0Reznik, J.\u00a0Cappos, and R.\u00a0Bhaskar (2018) Design of activity recognition systems with wearable sensors. In: Proceeding of IEEE Sensors Applications Symposium (SAS), Seoul, pp 226-231","DOI":"10.1109\/SAS.2018.8336752"},{"issue":"S1","key":"2015_CR3","first-page":"132","volume":"17","author":"G Biagetti","year":"2018","unstructured":"Biagetti G, Crippa P, Falaschetti L, Orcioni S, Turchetti C (2018) Human activity monitoring system based on wearable semg and accelerometer wireless sensor nodes. Biomed Eng 17(S1):132","journal-title":"Biomed Eng"},{"issue":"11","key":"2015_CR4","doi-asserted-by":"publisher","DOI":"10.2196\/11335","volume":"6","author":"J Il-Young","year":"2018","unstructured":"Il-Young J, Hae R, Kim E, Lee H-W, Jung H (2018) Impact of a wearable device-based walking programs in rural older adults on physical activity and health outcomes: Cohort study. Jmir Mhealth Uhealth 6(11):e11335. https:\/\/doi.org\/10.2196\/11335","journal-title":"Jmir Mhealth Uhealth"},{"key":"2015_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.cmpb.2017.10.008","volume":"153","author":"S Kekade","year":"2018","unstructured":"Kekade S, Hseieh CH, Islam MM, Atique S, Shabbir SA (2018) The usefulness and actual use of wearable devices among the elderly population. Comput Methods Progr Biomed 153:137\u2013159. https:\/\/doi.org\/10.1016\/j.cmpb.2017.10.008","journal-title":"Comput Methods Progr Biomed"},{"key":"2015_CR6","doi-asserted-by":"crossref","unstructured":"Babiker M, Khalifa OO, Htike KK, Hassan A, Zaharadeen M (2017) Automated daily human activity recognition for video surveillance using neural network. In: Proceeding of IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Putrajaya, pp 1-5","DOI":"10.1109\/ICSIMA.2017.8312024"},{"key":"2015_CR7","unstructured":"Raya JAZ, V\u00e1zquez MSG, M\u00e9ndez JCJ, Obeso, AM, Acosta ALR (2019) Semantic segmentation in egocentric video frames with deep learning for recognition of activities of daily living. In: Proceeding of Applications of Machine Learning, San Diego, p 1113909"},{"key":"2015_CR8","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1007\/s11042-018-6034-1","volume":"79","author":"KS Kumar","year":"2018","unstructured":"Kumar KS, Bhavani R (2018) Human activity recognition in egocentric video using hog, gist and color features. Multimed Tools Appl 79:3543\u20133559","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"2015_CR9","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1109\/JSAC.2017.2679658","volume":"35","author":"W Wang","year":"2017","unstructured":"Wang W, Liu AX, Shahzad M, Ling K, Lu S (2017) Device-free human activity recognition using commercial wifi devices. IEEE J Sel Areas Commun 35(5):1118\u20131131. https:\/\/doi.org\/10.1109\/JSAC.2017.2679658","journal-title":"IEEE J Sel Areas Commun"},{"key":"2015_CR10","doi-asserted-by":"crossref","unstructured":"Ibrahim OA, Keller J, Popescu M (2017) Context preserving representation of daily activities in elder care. In: Proceeding of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas, pp 547\u2013551","DOI":"10.1109\/BIBM.2017.8217707"},{"key":"2015_CR11","doi-asserted-by":"crossref","unstructured":"Bouazizi M, Ohtsuki T (2020) An infrared array sensor-based method for localizing and counting people for health care and monitoring. In: Proceeding of 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in Conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. Montreal, pp 4151\u20134155","DOI":"10.1109\/EMBC44109.2020.9176199"},{"issue":"20","key":"2015_CR12","doi-asserted-by":"publisher","first-page":"5957","DOI":"10.3390\/s20205957","volume":"20","author":"S Tateno","year":"2020","unstructured":"Tateno S, Meng F, Qian R, Hachiya Y (2020) Privacy-preserved fall detection method with three-dimensional convolutional neural network using low-resolution infrared array sensor. Sensors 20(20):5957. https:\/\/doi.org\/10.3390\/s20205957","journal-title":"Sensors"},{"key":"2015_CR13","doi-asserted-by":"publisher","first-page":"82563","DOI":"10.1109\/ACCESS.2021.3084926","volume":"9","author":"KA Muthukumar","year":"2021","unstructured":"Muthukumar KA, Bouazizi M, Ohtsuki T (2021) A novel hybrid deep learning model for activity detection using wide-angle low-resolution infrared array sensor. IEEE Access 9:82563\u201382576. https:\/\/doi.org\/10.1109\/ACCESS.2021.3084926","journal-title":"IEEE Access"},{"key":"2015_CR14","doi-asserted-by":"crossref","unstructured":"Kawashima T, Kawanishi Y, Ide I, Murase H, Kawade M (2017) Action recognition from extremely low-resolution thermal image sequence. In: Proceeding of 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, pp 1\u20136","DOI":"10.1109\/AVSS.2017.8078497"},{"key":"2015_CR15","doi-asserted-by":"crossref","unstructured":"Gochoo M, Tan TH, Batjargal T, Seredin O, Huang SC (2018) Device-free non-privacy invasive indoor human posture recognition using low-resolution infrared sensor-based wireless sensor networks and dcnn. In: Proceeding of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, pp 2311\u20132316","DOI":"10.1109\/SMC.2018.00397"},{"key":"2015_CR16","doi-asserted-by":"crossref","unstructured":"Shih CS, Wang YT, Chou JJ (2020) Multiple-image super- resolution for networked extremely low-resolution thermal sensor array. In: Proceeding of IEEE Second Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML), Sydney pp 1\u20136","DOI":"10.1109\/SenSysML50931.2020.00004"},{"key":"2015_CR17","doi-asserted-by":"crossref","unstructured":"Adolf J, Macas M, Lhotska L, Dolezal J (2018) Deep neural network based body posture recognitions and fall detection from low resolution infrared array sensor. In: Proceeding of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, pp 2394\u20132399","DOI":"10.1109\/BIBM.2018.8621582"},{"key":"2015_CR18","doi-asserted-by":"crossref","unstructured":"Fujita H, Otsuka S (2018) Posture detection for elderly using infrared array sensor and fine tuning. In: Proceeding of IEEE Visual Communications and Image Processing (VCIP), Taichung, pp 1\u20134","DOI":"10.1109\/VCIP.2018.8698710"},{"issue":"10","key":"2015_CR19","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.3390\/s21103551","volume":"21","author":"C Yin","year":"2021","unstructured":"Yin C, Chen J, Miao X, Jiang H, Chen D (2021) Device-free human activity recognition with low-resolution infrared array sensor using long short-term memory neural network. Sensors 21(10):3551. https:\/\/doi.org\/10.3390\/s21103551","journal-title":"Sensors"},{"key":"2015_CR20","doi-asserted-by":"crossref","unstructured":"Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B (2016) Deepercut: a deeper, stronger, and faster multi-person pose estimation model. In: Proceeding of the European Conference on Computer Vision (ECCV), Amsterdam, pp 34\u201350","DOI":"10.1007\/978-3-319-46466-4_3"},{"key":"2015_CR21","unstructured":"Zhe C, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, pp 7291\u20137299"},{"key":"2015_CR22","doi-asserted-by":"crossref","unstructured":"Tekin B, Marquez-Neila P, Salzmann M, Wei, Fua P (2017) Learning to fuse 2D and 3D image cues for monocular body pose estimation. In: Proceeding of IEEE International Conference on Computer Vision (ICCV), Italy, pp 3961\u20133970","DOI":"10.1109\/ICCV.2017.425"},{"key":"2015_CR23","doi-asserted-by":"crossref","unstructured":"Wang K, Wang Q, Xue F, Chen W (2020) 3D-skeleton estimation based on commodity millimeter wave radar. In: Proceeding of IEEE 6th International Conference on Computer and Communications (ICCC), China, pp 1339\u20131343","DOI":"10.1109\/ICCC51575.2020.9345237"},{"issue":"20","key":"2015_CR24","doi-asserted-by":"publisher","first-page":"23174","DOI":"10.1109\/JSEN.2021.3107361","volume":"21","author":"W Ding","year":"2021","unstructured":"Ding W, Cao Z, Zhang J, Chen R, Guo X, Wang G (2021) Radar-based 3D human skeleton estimation by kinematic constrained learning. IEEE Sens 21(20):23174\u201323184. https:\/\/doi.org\/10.1109\/JSEN.2021.3107361","journal-title":"IEEE Sens"},{"key":"2015_CR25","doi-asserted-by":"crossref","unstructured":"Wang F, Zhou S, Panev S, Han J, Huang D (2019) Person-in-WiFi: fine-grained person perception using wifi. In: Proceeding of IEEE\/CVF International Conference on Computer Vision (ICCV), Korea, pp 5451\u20135460","DOI":"10.1109\/ICCV.2019.00555"},{"key":"2015_CR26","doi-asserted-by":"crossref","unstructured":"Jiang W, Xue H, Miao C, Wang S, Lin S, Tian C, Murali S, Hu H, Su Z, Su L (2020) Towards 3D human pose construction using wifi. In: Proceeding of the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), United Kingdom, pp 1\u201314","DOI":"10.1145\/3372224.3380900"},{"key":"2015_CR27","doi-asserted-by":"crossref","unstructured":"Iwata S, Kawanishi Y, Deguchi D, Ide I, Aizawa T(2021) LFIR2Pose: pose estimation from an extremely low-resolution FIR image sequence. In: Proceeding of 25th International Conference on Pattern Recognition (ICPR), Italy, pp 2597\u20132603","DOI":"10.1109\/ICPR48806.2021.9412484"},{"key":"2015_CR28","doi-asserted-by":"crossref","unstructured":"Agustiono W, Utoyo MI, Rulaningtyas R, Satoto BD (2020) A modification of convolutional neural network layer to increase images classification accuracy. In: Proceeding of 6th Information Technology International Seminar (ITIS), Surabaya, pp 274\u2013279","DOI":"10.1109\/ITIS50118.2020.9321011"},{"key":"2015_CR29","doi-asserted-by":"crossref","unstructured":"Cho K, Merrienboer BV, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. In: Proceeding of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Qatar, pp 1724\u20131734","DOI":"10.3115\/v1\/D14-1179"},{"key":"2015_CR30","doi-asserted-by":"crossref","unstructured":"Jabreel M, Moreno A (2017) Target-dependent sentiment analysis of tweets using a bi-directional gated recurrent unit. In: Proceeding of 13th International Conference on Web Information Systems and Technologies, Portugal, pp 80\u201387","DOI":"10.5220\/0006299900800087"},{"issue":"44","key":"2015_CR31","doi-asserted-by":"publisher","first-page":"28579","DOI":"10.1021\/acsomega.0c03417","volume":"5","author":"R Liang","year":"2020","unstructured":"Liang R, Chang X, Jia P, Xu C (2020) Mine gas concentration forecasting model based on an optimized bigru network. ACS Omega 5(44):28579\u201328586. https:\/\/doi.org\/10.1021\/acsomega.0c03417","journal-title":"ACS Omega"},{"key":"2015_CR32","doi-asserted-by":"crossref","unstructured":"Zhao X, Shao Y, Mai J, Yin A, Xu S (2020) Respiratory sound classification based on bigru-attention network with xgboost. In: Proceeding of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 915\u2013920","DOI":"10.1109\/BIBM49941.2020.9313506"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02015-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02015-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02015-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T14:27:04Z","timestamp":1712932024000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02015-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["2015"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02015-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]},"assertion":[{"value":"19 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 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":"Conflict of interest"}}]}}