{"id":"W4402351396","doi":"10.1109/ijcnn60899.2024.10650496","title":"Vision-based Spatiotemporal Learning for Human Activity Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Activity recognition; Artificial intelligence; Human–computer interaction; Computer vision","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":[0.0003107405,0.0005259795,0.0004515017,0.000670617,0.0001138399,0.0003470477,0.0005607345,0.0003621256,0.001540695],"category_scores_gemma":[0.001215454,0.0001711985,0.0004654701,0.0009695644,0.0002349845,0.000792552,0.0004469131,0.0005705772,0.0007950227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003718104,"about_ca_system_score_gemma":0.0003707036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004041742,"about_ca_topic_score_gemma":0.006340414,"domain_scores_codex":[0.9996979,0.00005495735,0.00001856448,0.0001294127,0.00006777569,0.00003129584],"domain_scores_gemma":[0.9997471,0.00007146975,0.00003895174,0.00005087508,0.00007529778,0.00001624384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002515289,0.0002004843,0.004274609,0.000191217,0.0001077572,0.0001255979,0.00007903633,0.09953712,0.04487213,0.003779911,0.005783594,0.840797],"study_design_scores_gemma":[0.00000707236,0.00008152694,0.003253489,0.00001495844,0.00001738055,0.0001244783,0.00003205565,0.9768319,0.0114066,0.005738846,0.00247823,0.00001348806],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0211802,0.0009336433,0.9738312,0.0001424783,0.00009650259,0.00003410256,0.0005178922,0.001728496,0.001535464],"genre_scores_gemma":[0.7322577,0.001106849,0.2611641,0.0002572355,0.0001208195,0.00009637817,0.001910523,0.0001092186,0.00297728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004041742,"threshold_uncertainty_score":0.008036435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04090917300522919,"score_gpt":0.3207537552289642,"score_spread":0.279844582223735,"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."}}