{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:46:01Z","timestamp":1776941161068,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,23]]},"DOI":"10.1145\/3802842.3802900","type":"proceedings-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T07:13:08Z","timestamp":1776928388000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning for Physical Load Estimation: Insights from ViLoad Video Dataset"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1384-2095","authenticated-orcid":false,"family":"Lahya","sequence":"first","affiliation":[{"name":"EuroMov - Digital Health in Motion\u00a0 (Euromov DHM), Montpellier, France and IMT Mines Al\u00e8s, Al\u00e8s, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0643-9940","authenticated-orcid":false,"family":"Taleb-Salah","sequence":"additional","affiliation":[{"name":"EuroMov - Digital Health in Motion\u00a0 (Euromov DHM), Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1482-7442","authenticated-orcid":false,"family":"Fradi","sequence":"additional","affiliation":[{"name":"EuroMov - Digital Health in Motion\u00a0 (Euromov DHM), Montpellier, France and IMT Mines Al\u00e8s, Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8207-5369","authenticated-orcid":false,"family":"Slangen","sequence":"additional","affiliation":[{"name":"EuroMov - Digital Health in Motion\u00a0 (Euromov DHM), Montpellier, France and IMT Mines Al\u00e8s, Al\u00e8s, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0918-5788","authenticated-orcid":false,"family":"Montmain","sequence":"additional","affiliation":[{"name":"SyCoIA - Syst\u00e8mes Complexes et Intelligence Artificielle, IMT Mines Al\u00e8s, ALES, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1428-6199","authenticated-orcid":false,"family":"Ben-ammar","sequence":"additional","affiliation":[{"name":"SyCoIA - Syst\u00e8mes Complexes et Intelligence Artificielle, IMT Mines Al\u00e8s, Al\u00e8s, France"}]}],"member":"320","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"2019. Adaptable Workstations for Human\u2013Robot Collaboration: A Reconfigurable Framework for Improving Worker Ergonomics and Productivity. PP (March 2019) 1\u20131."},{"key":"e_1_3_3_1_3_2","unstructured":"Valentin Bazarevsky Ivan Grishchenko Karthik Raveendran Tyler Zhu Fan Zhang and Matthias Grundmann. 2020. BlazePose: On-device Real-time Body Pose tracking."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Zhe Cao Gines Hidalgo Tomas Simon Shih-En Wei and Yaser Sheikh. 2019. Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE transactions on pattern analysis and machine intelligence 43 1 (2019) 172\u2013186.","DOI":"10.1109\/TPAMI.2019.2929257"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.143"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Xinyu Chen Yantao Yu Yunpeng Wang and Zhen-Zhong Hu. 2025. Multimodal data fusion for ergonomic assessment of construction workers in visually obstructed environments. Automation in Construction 179 (2025) 106495.","DOI":"10.1016\/j.autcon.2025.106495"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yudi Dai Yitai Lin Xiping Lin Chenglu Wen Lan Xu Hongwei Yi Siqi Shen Yuexin Ma and Cheng Wang. 2023. SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments.","DOI":"10.1109\/CVPR52729.2023.00073"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Shradha Dubey and Manish Dixit. 2023. A comprehensive survey on human pose estimation approaches. Multimedia Systems 29 1 (2023) 167\u2013195.","DOI":"10.1007\/s00530-022-00980-0"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.256"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS56176.2022.9959545"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00685"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Sue Hignett and Lynn McAtamney. 2000. Rapid entire body assessment (REBA). Applied ergonomics 31 2 (2000) 201\u2013205.","DOI":"10.1016\/S0003-6870(99)00039-3"},{"key":"e_1_3_3_1_13_2","unstructured":"TensorFlow Hub. 2024. MoveNet: Ultra fast and accurate pose detection model."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Tianjian Jiang Johsan Billingham Sebastian M\u00fcksch Juan Zarate Nicolas Evans Martin Oswald Marc Pollefeys Otmar Hilliges Manuel Kaufmann and Jie Song. 2024. WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation. eccv (2024).","DOI":"10.1007\/978-3-031-72655-2_20"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Yunxiao Jiang Elsayed\u00a0M. Atwa Pengguang He Jinhui Zhang Mengzui Di Jinming Pan and Hongjian Lin. 2025. Computer Vision-Based Multi-Feature Extraction and Regression for Precise Egg Weight Measurement in Laying Hen Farms. Agriculture 15 19 (2025).","DOI":"10.3390\/agriculture15192035"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Sol Lim and Clive D\u2019Souza. 2020. Measuring effects of two-handed side and anterior load carriage on thoracic-pelvic coordination using wearable gyroscopes. Sensors 20 18 (2020) 5206.","DOI":"10.3390\/s20185206"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Mario Passalacqua Robert Pellerin Florian Magnani Philippe Doyon-Poulin Laur\u00e8ne Del-Aguila Jared Boasen and Pierre-Majorique L\u00e9ger. 2025. Human-centred AI in industry 5.0: a systematic review. International Journal of Production Research 63 7 (2025) 2638\u20132669.","DOI":"10.1080\/00207543.2024.2406021"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-00839-8_2"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Christian Pfitzner Stefan May and Andreas N\u00fcchter. 2018. Body Weight Estimation for Dose-Finding and Health Monitoring of Lying Standing and Walking Patients Based on RGB-D Data. Sensors (Basel) 18 5 (2018) 1311.","DOI":"10.3390\/s18051311"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Arafat Rahman Sol Lim and Seokhyun Chung. 2026. Fairness in machine learning-based hand load estimation: A case study on load carriage tasks. Applied Ergonomics 130 (2026) 104642.","DOI":"10.1016\/j.apergo.2025.104642"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Federico Roggio Bruno Trovato Martina Sortino and Giuseppe Musumeci. 2024. A comprehensive analysis of the machine learning pose estimation models used in human movement and posture analyses: A narrative review. Heliyon 10 21 (2024).","DOI":"10.1016\/j.heliyon.2024.e39977"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Filip Rybnik\u00e1r Ilona Ka\u010derov\u00e1 Petr Ho\u0159ej\u0161\u00ed and Michal \u0160imon. 2022. Ergonomics evaluation using motion capture technology\u2014literature review. Applied Sciences 13 1 (2022) 162.","DOI":"10.3390\/app13010162"},{"key":"e_1_3_3_1_23_2","volume-title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","author":"Sandler Mark","year":"2019","unstructured":"Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2019. MobileNetV2: Inverted Residuals and Linear Bottlenecks."},{"key":"e_1_3_3_1_24_2","unstructured":"Akram Shojaei Arash\u00a0Abbasi Larki Mehdi Delrobaei Hanieh Moradi and Yas Vaseghi. 2024. IMU-Based Motion Capture Data for Various Walking Tasks. (6 2024)."},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Rim Slama Ilhem Slama Houda Tlahig Pierre Slangen and Oussama Ben-Ammar. 2024. An overview on human-centred technologies measurements and optimisation in assembly systems. International Journal of Production Research 62 14 (2024) 5336\u20135358.","DOI":"10.1080\/00207543.2023.2286627"},{"key":"e_1_3_3_1_26_2","series-title":"Proceedings of Machine Learning Research","volume-title":"image2mass: Estimating the Mass of an Object from Its Image","volume":"78","author":"Standley Trevor","year":"2017","unstructured":"Trevor Standley, Ozan Sener, Dawn Chen, and Silvio Savarese. 2017. image2mass: Estimating the Mass of an Object from Its Image. Proceedings of Machine Learning Research, Vol.\u00a078. PMLR. 324\u2013333 pages."},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00584"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Yacine Taibi Yannick\u00a0A Metzler Silja Bellingrath and Andreas M\u00fcller. 2021. A systematic overview on the risk effects of psychosocial work characteristics on musculoskeletal disorders absenteeism and workplace accidents. Applied ergonomics 95 (2021) 103434.","DOI":"10.1016\/j.apergo.2021.103434"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Du Tran Heng Wang Lorenzo Torresani Jamie Ray Yann LeCun and Manohar Paluri. 2018. A Closer Look at Spatiotemporal Convolutions for Action Recognition.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"e_1_3_3_1_30_2","unstructured":"Jiaqi Wang Wenwei Zhang Xueyan Xu Yuhao Liu Zhihui Wei and Dahua Lin. 2023. RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose. (2023)."},{"key":"e_1_3_3_1_31_2","unstructured":"Chathura Wimalasiri and Prasan\u00a0Kumar Sahoo. 2024. Vision-Based Approach for Food Weight Estimation from 2D Images."},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Yufei Xu Jing Zhang Qiming Zhang and Dacheng Tao. 2022. Vitpose: Simple vision transformer baselines for human pose estimation. Advances in neural information processing systems 35 (2022) 38571\u201338584.","DOI":"10.52202\/068431-2795"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"Ziqian Yang Dechuan Song Jiachuan Ning and Zhihui Wu. 2024. A systematic review: Advancing ergonomic posture risk assessment through the integration of computer vision and machine learning techniques. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3509447"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"crossref","unstructured":"Junqi Zhao and Esther Obonyo. 2020. Convolutional long short-term memory model for recognizing construction workers\u2019 postures from wearable inertial measurement units. Advanced Engineering Informatics 46 (2020) 101177.","DOI":"10.1016\/j.aei.2020.101177"},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Ce Zheng Wenhan Wu Chen Chen Taojiannan Yang Sijie Zhu Ju Shen Nasser Kehtarnavaz and Mubarak Shah. 2023. Deep learning-based human pose estimation: A survey. ACM computing surveys 56 1 (2023) 1\u201337.","DOI":"10.1145\/3603618"}],"event":{"name":"MOCO '26: The 10th International Conference on Movement and Computing 2026","location":"Montpellier France","acronym":"MOCO '26"},"container-title":["Proceedings of the 10th International Conference on Movement and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3802842.3802900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:03:23Z","timestamp":1776938603000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3802842.3802900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":34,"alternative-id":["10.1145\/3802842.3802900","10.1145\/3802842"],"URL":"https:\/\/doi.org\/10.1145\/3802842.3802900","relation":{},"subject":[],"published":{"date-parts":[[2026,4,23]]},"assertion":[{"value":"2026-04-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}