{"id":"W2921929314","doi":"10.1109/tnsre.2019.2904477","title":"Online Learning of Gait Models From Older Adult Data","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Else Kröner-Fresenius-Stiftung; Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Gait; Computer science; Swing; Wearable computer; Representation (politics); Machine learning; Identification (biology); Accelerometer; Artificial intelligence; Gait analysis; Work (physics); Wearable technology; Physical medicine and rehabilitation; Engineering; Medicine","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":[0.00006740859,0.0001443491,0.0002418865,0.0001756954,0.00002994294,0.00002517996,0.00009151991,0.00007276055,0.00003219962],"category_scores_gemma":[0.000008315338,0.0001383398,0.00006768291,0.00017926,0.00001416267,0.0003015669,0.00000149205,0.0002052828,0.000008517095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002086137,"about_ca_system_score_gemma":0.000003158599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00013344,"about_ca_topic_score_gemma":0.00001014969,"domain_scores_codex":[0.9991586,0.00002450953,0.0003245503,0.0002214993,0.0001467706,0.0001241185],"domain_scores_gemma":[0.9993473,0.0002413884,0.00003488794,0.0002514936,0.00006399649,0.00006095292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003576934,0.00003065283,0.00006101414,0.0002431871,0.00005977292,2.537535e-7,0.0002657531,0.9890029,0.007652457,0.0000225971,0.00001031427,0.002647525],"study_design_scores_gemma":[0.0003298517,0.00004299277,0.0008474928,0.0002005607,0.00003661044,0.000001809684,0.0005524288,0.997516,0.0002488845,0.00000525617,0.0000741908,0.0001438702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7411807,0.0001396211,0.2576919,0.00002626424,0.0004563614,0.0001562954,0.000142423,0.0001701393,0.00003635448],"genre_scores_gemma":[0.9988029,0.00006931859,0.0009087751,0.000005038039,0.00003164439,0.00001253086,0.00005267715,0.0000300125,0.00008709599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2576222,"threshold_uncertainty_score":0.5641332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184786600504788,"score_gpt":0.2115032290823308,"score_spread":0.1996553630772829,"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."}}