{"id":"W4409364886","doi":"10.1609/aaai.v39i13.33567","title":"HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Centre Scientifique et Technique du Bâtiment; National Natural Science Foundation of China","keywords":"Pose; Estimation; Artificial intelligence; Units of measurement; Inertial measurement unit; Inertial frame of reference; Computer science; Computer vision; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000884006,0.001749219,0.001593325,0.0008951311,0.0002685909,0.000733998,0.002362107,0.001204878,0.003002895],"category_scores_gemma":[0.002060665,0.0008613914,0.001390359,0.00101889,0.0006543018,0.001452086,0.00250351,0.001552862,0.002399981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003294993,"about_ca_system_score_gemma":0.0008452946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004328764,"about_ca_topic_score_gemma":0.007038946,"domain_scores_codex":[0.999312,0.0001285636,0.00002596202,0.0002683919,0.0001912605,0.00007373891],"domain_scores_gemma":[0.9995071,0.0001497555,0.00005815318,0.0001706124,0.0000790761,0.00003535423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003084715,0.0001871663,0.002290629,0.0001948277,0.0003528813,0.0002235036,0.0001457387,0.2804457,0.01446407,0.003317833,0.007380759,0.6906884],"study_design_scores_gemma":[0.00002519447,0.0001120674,0.001069362,0.00001809806,0.00003442487,0.0001548429,0.00002846083,0.9862202,0.004571707,0.005013172,0.002724916,0.0000274756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008813409,0.0005061941,0.9850428,0.00007041971,0.0000592852,0.00007131683,0.0002901547,0.004477084,0.0006692217],"genre_scores_gemma":[0.3359604,0.000783571,0.6520098,0.0005414172,0.0001750579,0.0003679942,0.00373659,0.0005174477,0.005907742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004328764,"threshold_uncertainty_score":0.01004565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1912323440085459,"score_gpt":0.3205294049512579,"score_spread":0.1292970609427119,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}