{"id":"W1572369853","doi":"10.1609/aaai.v24i1.7724","title":"Activity and Gait Recognition with Time-Delay Embeddings","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Wearable computer; Gait; Activity recognition; Set (abstract data type); Embedding; Feature extraction; Mobile phone; Wearable technology; Artificial intelligence; Feature (linguistics); Data set; Stairs; Pattern recognition (psychology); Machine learning; Real-time computing; Embedded system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001739932,0.0001684483,0.000186399,0.0001021965,0.0001045243,0.0001139149,0.0002139355,0.00008599416,0.0004430802],"category_scores_gemma":[0.00008951747,0.0001215818,0.00005598526,0.0002983052,0.0001902947,0.0002243758,0.00003693239,0.0003703762,0.0001420534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001108159,"about_ca_system_score_gemma":0.00001383354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001942027,"about_ca_topic_score_gemma":0.00003472887,"domain_scores_codex":[0.9991882,0.000004131067,0.0001999701,0.0002173065,0.0002032887,0.0001870361],"domain_scores_gemma":[0.9993975,0.00003878604,0.00009997695,0.00009123305,0.0002955754,0.00007693622],"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":[0.00006293551,0.00006679112,0.0001857106,0.00005904217,0.00004265781,3.798335e-7,0.0003502012,0.00003408089,0.8007593,0.005504558,0.0001022158,0.1928321],"study_design_scores_gemma":[0.00003149245,0.00008237745,0.0003785017,0.0001352667,0.00005151975,0.00001159145,0.000207799,0.06735812,0.9172202,0.01421049,0.0000764355,0.0002362516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797638,0.000002906674,0.0004475138,0.0003157835,0.00009105536,0.0001482733,0.000009435629,0.0001117358,0.01910948],"genre_scores_gemma":[0.9990857,0.0000270962,0.0005971499,0.00003732941,0.00004744033,0.0000158248,0.000001154087,0.00001800766,0.0001702468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1925958,"threshold_uncertainty_score":0.4957962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02953149173377507,"score_gpt":0.2416490288629769,"score_spread":0.2121175371292019,"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."}}