{"doi":"10.1109/thms.2025.3539187","title":"A Single-Camera Method for Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms","abstract":"A computer vision (CV) method to automatically measure the revised NIOSH lifting equation asymmetry angle (<italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">A</i>) from a single camera is described and tested. A laboratory study involving ten participants performing various lifts was used to estimate <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">A</i> in comparison to ground truth joint coordinates obtained using 3-D motion capture (MoCap). To address challenges, such as obstructed views and limitations in camera placement in real-world scenarios, the CV method utilized video-derived coordinates from a selected set of landmarks. A 2-D pose estimator (HR-Net) detected landmark coordinates in each video frame, and a 3-D algorithm (VideoPose3D) estimated the depth of each 2-D landmark by analyzing its trajectories. The mean absolute precision error for the CV method, compared to MoCap measurements using the same subset of landmarks for estimating <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">A</i>, was 6.25° (SD = 10.19°, N = 360). The mean absolute accuracy error of the CV method, compared against conventional MoCap landmark markers was 9.45° (SD = 14.01°, <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">N</i> = 360).","journal":"IEEE Transactions on Human-Machine Systems","year":2025,"id":520637,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9629,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1390554,"name":"Zitong Zhan","orcid":null,"position":1,"is_corresponding":false},{"id":254414,"name":"Huan Xu","orcid":"0000-0003-1696-4608","position":2,"is_corresponding":false},{"id":559291,"name":"Yin Li","orcid":"0000-0003-4173-9453","position":3,"is_corresponding":false},{"id":363693,"name":"Yu Hen Hu","orcid":"0000-0003-3427-0677","position":4,"is_corresponding":false},{"id":363689,"name":"Ming‐Lun Lu","orcid":"0000-0002-8291-9111","position":5,"is_corresponding":false},{"id":1390555,"name":"Dwight Werren","orcid":null,"position":6,"is_corresponding":false},{"id":363694,"name":"Robert G. Radwin","orcid":"0000-0002-7973-0641","position":7,"is_corresponding":false},{"id":1360464,"name":"Zhengyang Lou","orcid":null,"position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T02:49:28.470782Z","pmid":"40160534","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}