{"id":"W2032277247","doi":"10.1177/0278364911426178","title":"Incremental learning of full body motion primitives and their sequencing through human motion observation","year":2011,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"IRT Foundation","keywords":"Motion (physics); Artificial intelligence; Computer vision; Computer science; Motion capture; Humanoid robot; Motion field; Motion estimation; Graph; Structure from motion; Hidden Markov model; Robot; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001851874,0.00007130026,0.0001039384,0.0002388335,0.0002072426,0.0001161736,0.0006311336,0.00003790456,0.00002494218],"category_scores_gemma":[0.0001549013,0.00005048536,0.00005532496,0.0001597458,0.0001153373,0.001001483,0.000209137,0.0004273978,0.000003752175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475586,"about_ca_system_score_gemma":0.00006267693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007333596,"about_ca_topic_score_gemma":0.000007453792,"domain_scores_codex":[0.9983695,0.0002925415,0.0003652557,0.0001120313,0.0007298404,0.0001307902],"domain_scores_gemma":[0.9980628,0.0001659771,0.0003384545,0.0001028536,0.001295374,0.00003449476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001634415,0.000371033,0.006110481,0.00004309657,0.000365097,0.00003269819,0.02792032,0.004438254,0.7409543,0.1669792,0.0001618203,0.05246018],"study_design_scores_gemma":[0.002098994,0.002062259,0.08812334,0.0007000655,0.00003743002,0.0008548791,0.01076167,0.1138903,0.5416909,0.2392356,0.0001712039,0.0003734154],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6163179,0.00004125093,0.3818052,0.00051436,0.0001740594,0.00007277081,8.372004e-7,0.000009058714,0.001064552],"genre_scores_gemma":[0.9916205,0.0001134741,0.008028783,0.00003329627,0.0001468834,0.000001293542,0.000003221144,0.00000581262,0.00004676305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3753026,"threshold_uncertainty_score":0.2058733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2475646891276398,"score_gpt":0.3767040064118809,"score_spread":0.1291393172842411,"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."}}