{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T17:04:49Z","timestamp":1762794289672,"version":"build-2065373602"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000125","name":"National Institute for Occupational Safety and Health","doi-asserted-by":"publisher","award":["R01OH012313","R01OH012313","R01OH012313"],"award-info":[{"award-number":["R01OH012313","R01OH012313","R01OH012313"]}],"id":[{"id":"10.13039\/100000125","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Manual material handling tasks, such as lifting and lowering, are ubiquitous across industry sectors. Overexertion during these tasks is among the leading causes of workplace injuries. Previous studies have shown that lifting load is a key factor in determining the risk of injury. However, existing methods for measuring the lifting load often rely on manual measurements, sensor fusion, or other techniques that are difficult to scale in practice. In this study, we present a vision-based approach to automatically predict lifting load by analyzing human body pose trajectories extracted from video alone. Specifically, our method employs person detection, visual tracking, and human body pose estimation to extract pose trajectories and their kinematic features, which are then used to train a Transformer model for load prediction. To evaluate our method, we conducted a human subjects study of 19 participants performing various lifting and lowering tasks with varying postures. Our method achieved an average accuracy of 74.8% to distinguish between light vs. heavy objects, and an average accuracy of 50.8% to identify three levels of lifting loads (light, medium, heavy) across lifting and lowering tasks. These results demonstrate a first step towards computer vision based solutions for automatic, noninvasive, scalable injury risk assessment for manual material handling tasks.<\/jats:p>","DOI":"10.1007\/s00138-025-01758-w","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:41:35Z","timestamp":1761583295000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards video-based injury risk assessment: predicting lifting loads from body pose trajectories"],"prefix":"10.1007","volume":"36","author":[{"given":"Zihao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangzhou","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Radwin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"1758_CR1","unstructured":"Insurance, L.M.B.: 2024 Workplace Safety Index (2024). https:\/\/business.libertymutual.com\/insights\/2024-workplace-safety-index\/"},{"issue":"7","key":"1758_CR2","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1080\/00140139308967940","volume":"36","author":"TR Waters","year":"1993","unstructured":"Waters, T.R., Putz-Anderson, V., Garg, A., Fine, L.J.: Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36(7), 749\u2013776 (1993)","journal-title":"Ergonomics"},{"issue":"5","key":"1758_CR3","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1177\/0018720815623894","volume":"58","author":"M-L Lu","year":"2016","unstructured":"Lu, M.-L., Putz-Anderson, V., Garg, A., Davis, K.G.: Evaluation of the impact of the revised national institute for occupational safety and health lifting equation. Hum. Factors 58(5), 667\u2013682 (2016)","journal-title":"Hum. Factors"},{"key":"1758_CR4","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.apergo.2017.02.020","volume":"65","author":"RL Greene","year":"2017","unstructured":"Greene, R.L., Azari, D.P., Hu, Y.H., Radwin, R.G.: Visualizing stressful aspects of repetitive motion tasks and opportunities for ergonomic improvements using computer vision. Appl. Ergon. 65, 461\u2013472 (2017)","journal-title":"Appl. Ergon."},{"key":"1758_CR5","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.apergo.2019.05.004","volume":"80","author":"A Abobakr","year":"2019","unstructured":"Abobakr, A., Nahavandi, D., Hossny, M., Iskander, J., Attia, M., Nahavandi, S., Smets, M.: Rgb-d ergonomic assessment system of adopted working postures. Appl. Ergon 80, 75\u201388 (2019). https:\/\/doi.org\/10.1016\/j.apergo.2019.05.004","journal-title":"Appl. Ergon"},{"key":"1758_CR6","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.apergo.2017.02.015","volume":"65","author":"VM Manghisi","year":"2017","unstructured":"Manghisi, V.M., Uva, A.E., Fiorentino, M., Bevilacqua, V., Trotta, G.F., Monno, G.: Real time Rula assessment using Kinect v2 sensor. Appl. Ergon. 65, 481\u2013491 (2017). https:\/\/doi.org\/10.1016\/j.apergo.2017.02.015","journal-title":"Appl. Ergon."},{"issue":"2","key":"1758_CR7","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1109\/THMS.2022.3148339","volume":"52","author":"G Zhou","year":"2022","unstructured":"Zhou, G., Aggarwal, V., Yin, M., Yu, D.: A computer vision approach for estimating lifting load contributors to injury risk. IEEE Trans. Hum. Mach. Syst. 52(2), 207\u2013219 (2022). https:\/\/doi.org\/10.1109\/THMS.2022.3148339","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"1758_CR8","unstructured":"European Parliament, Council of the European Union: Regulation (EU) 2016\/679 of the European Parliament and of the Council. URL: https:\/\/data.europa.eu\/eli\/reg\/2016\/679\/oj Accessed 2023-04-13"},{"issue":"1","key":"1758_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jelekin.2008.12.001","volume":"20","author":"WS Marras","year":"2010","unstructured":"Marras, W.S., Lavender, S.A., Ferguson, S.A., Splittstoesser, R.E., Yang, G., Schabo, P.: Instrumentation for measuring dynamic spinal load moment exposures in the workplace. J. Electromyogr. Kinesiol. 20(1), 1\u20139 (2010). https:\/\/doi.org\/10.1016\/j.jelekin.2008.12.001","journal-title":"J. Electromyogr. Kinesiol."},{"issue":"1","key":"1758_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3603618","volume":"56","author":"C Zheng","year":"2023","unstructured":"Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz, N., Shah, M.: Deep learning-based human pose estimation: a survey. ACM Comput. Surv. 56(1), 1\u201337 (2023)","journal-title":"ACM Comput. Surv."},{"key":"1758_CR11","volume-title":"Fundamentals of Biomechanics","author":"D Knudson","year":"2007","unstructured":"Knudson, D.: Fundamentals of Biomechanics. Springer, New York (2007)"},{"issue":"2","key":"1758_CR12","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1177\/001872089103300201","volume":"33","author":"WS Marras","year":"1991","unstructured":"Marras, W.S., Sommerich, C.M.: A three-dimensional motion model of loads on the lumbar spine: I. model structure. Hum. Factors 33(2), 123\u2013137 (1991)","journal-title":"Hum. Factors"},{"issue":"45","key":"1758_CR13","doi-asserted-by":"publisher","first-page":"18327","DOI":"10.1073\/pnas.1306572110","volume":"110","author":"PW Battaglia","year":"2013","unstructured":"Battaglia, P.W., Hamrick, J.B., Tenenbaum, J.B.: Simulation as an engine of physical scene understanding. Proc. Natl. Acad. Sci. 110(45), 18327\u201318332 (2013). https:\/\/doi.org\/10.1073\/pnas.1306572110","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"3","key":"1758_CR14","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1080\/00222895.1991.10118362","volume":"23","author":"PL Weir","year":"1991","unstructured":"Weir, P.L., MacKenzie, C.L., Marteniuk, R.G., Cargoe, S.L., Frazer, M.B.: The effects of object weight on the kinematics of prehension. J. Motor Behav. 23(3), 192\u2013204 (1991). https:\/\/doi.org\/10.1080\/00222895.1991.10118362","journal-title":"J. Motor Behav."},{"key":"1758_CR15","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1037\/0096-1523.7.4.733","volume":"7","author":"S Runeson","year":"1981","unstructured":"Runeson, S., Frykholm, G.: Visual-perception of lifted weight. J. Exp. Psychol. Hum. Percept. Perform. 7, 733\u201340 (1981). https:\/\/doi.org\/10.1037\/0096-1523.7.4.733","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"issue":"11","key":"1758_CR16","doi-asserted-by":"publisher","first-page":"0224979","DOI":"10.1371\/journal.pone.0224979","volume":"14","author":"A Sciutti","year":"2019","unstructured":"Sciutti, A., Patan\u00e8, L., Sandini, G.: Development of visual perception of others\u2019 actions: children\u2019s judgment of lifted weight. PLoS One 14(11), 0224979 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0224979. (Publisher: Public Library of Science)","journal-title":"PLoS One"},{"issue":"1","key":"1758_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00426-005-0032-4","volume":"71","author":"AF Hamilton","year":"2007","unstructured":"Hamilton, A.F., Joyce, D.W., Flanagan, J.R., Frith, C.D., Wolpert, D.M.: Kinematic cues in perceptual weight judgement and their origins in box lifting. Psychol. Res. 71(1), 13\u201321 (2007). https:\/\/doi.org\/10.1007\/s00426-005-0032-4","journal-title":"Psychol. Res."},{"issue":"1","key":"1758_CR18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1207\/s15326969eco0501_2","volume":"5","author":"GP Bingham","year":"1993","unstructured":"Bingham, G.P.: Scaling judgments of lifted weight: lifter size and the role of the standard. Ecol. Psychol. 5(1), 31\u201364 (1993). https:\/\/doi.org\/10.1207\/s15326969eco0501_2","journal-title":"Ecol. Psychol."},{"key":"1758_CR19","doi-asserted-by":"publisher","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple Baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018 vol. 11210, pp. 472\u2013487. Springer, Cham (2018).https:\/\/doi.org\/10.1007\/978-3-030-01231-1_29","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"1758_CR20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"issue":"4","key":"1758_CR21","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"issue":"8","key":"1758_CR22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"6","key":"1758_CR23","first-page":"248","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graphics (Proc. SIGGRAPH Asia) 34(6), 248\u2013124816 (2015)","journal-title":"ACM Trans. Graphics (Proc. SIGGRAPH Asia)"},{"issue":"4474","key":"1758_CR24","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1126\/science.210.4474.1139","volume":"210","author":"M McCloskey","year":"1980","unstructured":"McCloskey, M., Caramazza, A., Green, B.: Curvilinear motion in the absence of external forces: naive beliefs about the motion of objects. Science 210(4474), 1139\u20131141 (1980). https:\/\/doi.org\/10.1126\/science.210.4474.1139","journal-title":"Science"},{"issue":"4","key":"1758_CR25","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1037\/0278-7393.9.4.636","volume":"9","author":"M McCloskey","year":"1983","unstructured":"McCloskey, M., Washburn, A., Felch, L.: Intuitive physics: The straight-down belief and its origin. J. Exp. Psychol. Learn. Mem. Cogn. 9(4), 636\u2013649 (1983). https:\/\/doi.org\/10.1037\/0278-7393.9.4.636","journal-title":"J. Exp. Psychol. Learn. Mem. Cogn."},{"issue":"2","key":"1758_CR26","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1037\/a0031912","volume":"120","author":"AN Sanborn","year":"2013","unstructured":"Sanborn, A.N., Mansinghka, V.K., Griffiths, T.L.: Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychol. Rev. 120(2), 411\u2013437 (2013). https:\/\/doi.org\/10.1037\/a0031912","journal-title":"Psychol. Rev."},{"issue":"6022","key":"1758_CR27","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1126\/science.1192788","volume":"331","author":"JB Tenenbaum","year":"2011","unstructured":"Tenenbaum, J.B., Kemp, C., Griffiths, T.L., Goodman, N.D.: How to grow a mind: statistics, structure, and abstraction. Science 331(6022), 1279\u20131285 (2011). https:\/\/doi.org\/10.1126\/science.1192788","journal-title":"Science"},{"key":"1758_CR28","unstructured":"Bates, C.J., Yildirim, I., Tenenbaum, J.B., Battaglia, P.W.: Humans predict liquid dynamics using probabilistic simulation"},{"key":"1758_CR29","unstructured":"Wu, J., Yildirim, I., Lim, J.J., Freeman, B., Tenenbaum, J.: Galileo: Perceiving physical object properties by integrating a physics engine with deep learning. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 127\u2013135. Curran Associates, Inc., Red Hook, NY, USA (2015). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2015\/file\/d09bf41544a3365a46c9077ebb5e35c3-Paper.pdf"},{"key":"1758_CR30","unstructured":"Liang, W., Zhao, Y., Zhu, Y., Zhu, S.-C.: Evaluating human cognition of containing relations with physical simulation. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 37 (2015)"},{"issue":"6033","key":"1758_CR31","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1126\/science.1196404","volume":"332","author":"E T\u00e9gl\u00e1s","year":"2011","unstructured":"T\u00e9gl\u00e1s, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J.B., Bonatti, L.L.: Pure reasoning in 12-month-old infants as probabilistic inference. Science 332(6033), 1054\u20131059 (2011). https:\/\/doi.org\/10.1126\/science.1196404","journal-title":"Science"},{"key":"1758_CR32","doi-asserted-by":"publisher","unstructured":"Li, Z., Sedlar, J., Carpentier, J., Laptev, I., Mansard, N., Sivic, J.: Estimating 3D motion and forces of person-object interactions from monocular video. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8632\u20138641 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00884 . arXiv:1904.02683 [cs]","DOI":"10.1109\/CVPR.2019.00884"},{"key":"1758_CR33","doi-asserted-by":"crossref","unstructured":"Ehsani, K., Tulsiani, S., Gupta, S., Farhadi, A., Gupta, A.: Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects. arXiv. arXiv:2003.12045 [cs] (2020)","DOI":"10.1109\/CVPR42600.2020.00030"},{"issue":"12","key":"1758_CR34","doi-asserted-by":"publisher","first-page":"2883","DOI":"10.1109\/TPAMI.2017.2759736","volume":"40","author":"T-H Pham","year":"2018","unstructured":"Pham, T.-H., Kyriazis, N., Argyros, A.A., Kheddar, A.: Hand-object contact force estimation from markerless visual tracking. IEEE Trans. Pattern Anal. Mach. Intel. 40(12), 2883\u20132896 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2759736","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"issue":"4","key":"1758_CR35","doi-asserted-by":"publisher","first-page":"11426","DOI":"10.1109\/LRA.2022.3199684","volume":"7","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Held, D., Erickson, Z.: Visual haptic reasoning: estimating contact forces by observing deformable object interactions. IEEE Robot. Autom. Lett. 7(4), 11426\u201311433 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"1758_CR36","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.apergo.2016.10.015","volume":"65","author":"P Plantard","year":"2017","unstructured":"Plantard, P., Shum, H.P.H., Le Pierres, A.-S., Multon, F.: Validation of an ergonomic assessment method using kinect data in real workplace conditions. Appl. Ergon. 65, 562\u2013569 (2017). https:\/\/doi.org\/10.1016\/j.apergo.2016.10.015","journal-title":"Appl. Ergon."},{"key":"1758_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2023.103440","volume":"95","author":"H Yuan","year":"2023","unstructured":"Yuan, H., Zhou, Y.: Ergonomic assessment based on monocular RGB camera in elderly care by a new multi-person 3d pose estimation technique (romp). Int. J. Ind. Ergon. 95, 103440 (2023). https:\/\/doi.org\/10.1016\/j.ergon.2023.103440","journal-title":"Int. J. Ind. Ergon."},{"key":"1758_CR38","doi-asserted-by":"publisher","unstructured":"Belabzioui, H.O., Plantard, P., Pontonnier, C., Dumont, G., Multon, F.: Impact of introducing sparse inertial measurement units in computer vision-based motion capture systems for ergonomic postural assessment. In: AHFE Conference Proceedings, vol. 189. Orlando, United States, pp. 1\u20139 (2025). https:\/\/doi.org\/10.54941\/ahfe1006571 . https:\/\/inria.hal.science\/hal-05042784","DOI":"10.54941\/ahfe1006571"},{"key":"1758_CR39","doi-asserted-by":"publisher","first-page":"103138","DOI":"10.1016\/j.apergo.2020.103138","volume":"87","author":"L Li","year":"2020","unstructured":"Li, L., Martin, T., Xu, X.: A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders. Appl. Ergon. 87, 103138 (2020). https:\/\/doi.org\/10.1016\/j.apergo.2020.103138","journal-title":"Appl. Ergon."},{"key":"1758_CR40","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.patcog.2017.11.007","volume":"76","author":"P Li","year":"2018","unstructured":"Li, P., Wang, D., Wang, L., Lu, H.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323\u2013338 (2018)","journal-title":"Pattern Recogn."},{"key":"1758_CR41","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"1758_CR42","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Computer vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740\u2013755 (2014). Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"1","key":"1758_CR43","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1177\/1071181320641230","volume":"64","author":"Y Li","year":"2020","unstructured":"Li, Y., Greene, R.L., Mu, F., Hu, Y.H., Radwin, R.G.: Towards video-based automatic lifting load prediction. Proc. Hum. Factors. Ergon. Soc. Ann. Meet. 64(1), 962\u2013963 (2020). https:\/\/doi.org\/10.1177\/1071181320641230","journal-title":"Proc. Hum. Factors. Ergon. Soc. Ann. Meet."},{"key":"1758_CR44","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"1758_CR45","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"1758_CR46","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/bf02295996","volume":"12","author":"Q McNemar","year":"1947","unstructured":"McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153\u2013157 (1947). https:\/\/doi.org\/10.1007\/bf02295996","journal-title":"Psychometrika"},{"key":"1758_CR47","doi-asserted-by":"crossref","unstructured":"Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: Scaling vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12104\u201312113 (2022)","DOI":"10.1109\/CVPR52688.2022.01179"},{"issue":"10s","key":"1758_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3490237","volume":"54","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Chen, J.: A survey on differential privacy for unstructured data content. ACM Comput. Surv. 54(10s), 1\u201328 (2022)","journal-title":"ACM Comput. Surv."},{"issue":"9","key":"1758_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3724113","volume":"57","author":"J Zhao","year":"2025","unstructured":"Zhao, J., Bagchi, S., Avestimehr, S., Chan, K., Chaterji, S., Dimitriadis, D., Li, J., Li, N., Nourian, A., Roth, H.: The federation strikes back: a survey of federated learning privacy attacks, defenses, applications, and policy landscape. ACM Comput. Surv. 57(9), 1\u201337 (2025)","journal-title":"ACM Comput. Surv."},{"key":"1758_CR50","doi-asserted-by":"crossref","unstructured":"Liao, Z., Zhu, J., Wang, C., Hu, H., Waslander, S.L.: Multiple view geometry transformers for 3d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 708\u2013717 (2024)","DOI":"10.1109\/CVPR52733.2024.00074"},{"key":"1758_CR51","doi-asserted-by":"crossref","unstructured":"Pavllo, D., Feichtenhofer, C., Grangier, D., Auli, M.: 3D human pose estimation in video with temporal convolutions and semi-supervised training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7753\u20137762 (2019)","DOI":"10.1109\/CVPR.2019.00794"},{"key":"1758_CR52","doi-asserted-by":"crossref","unstructured":"Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., Wang, Y.: MotionBERT: A unified perspective on learning human motion representations. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15085\u201315099 (2023)","DOI":"10.1109\/ICCV51070.2023.01385"},{"issue":"1","key":"1758_CR53","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1080\/00140139.2024.2304578","volume":"68","author":"AS Murugan","year":"2025","unstructured":"Murugan, A.S., Noh, G., Jung, H., Kim, E., Kim, K., You, H., Boufama, B.: Optimising computer vision-based ergonomic assessments: sensitivity to camera position and monocular 3d pose model. Ergonomics 68(1), 120\u2013137 (2025)","journal-title":"Ergonomics"},{"key":"1758_CR54","doi-asserted-by":"crossref","unstructured":"Werling, K., Omens, D., Lee, J., Exarchos, I., Liu, C.K.: Fast and feature-complete differentiable physics engine for articulated rigid bodies with contact constraints. In: Robotics: Science and Systems (2021)","DOI":"10.15607\/RSS.2021.XVII.034"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01758-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-025-01758-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-025-01758-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T17:01:00Z","timestamp":1762794060000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-025-01758-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1758"],"URL":"https:\/\/doi.org\/10.1007\/s00138-025-01758-w","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"21 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"136"}}