{"id":"W2791609610","doi":"10.1016/j.jbiomech.2018.01.034","title":"Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods","year":2018,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Accelerometer; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Computer science; Artificial intelligence; STRIDE; Gait","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":[0.001056448,0.0001318415,0.000241147,0.0004204176,0.0002782988,0.0003600623,0.0001732327,0.0001195697,0.00001592133],"category_scores_gemma":[0.0000859193,0.0001284991,0.00008576446,0.0005043462,0.00003688412,0.001657804,0.00007919309,0.0002446149,0.00000600687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002166285,"about_ca_system_score_gemma":0.00009857048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053888,"about_ca_topic_score_gemma":0.00000230259,"domain_scores_codex":[0.9987104,0.0002130162,0.0003763546,0.0002034997,0.0002992355,0.0001975228],"domain_scores_gemma":[0.9983044,0.0001631124,0.0007376783,0.0001583605,0.0005199774,0.0001164382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001434671,0.00004224048,0.000006604227,0.0000102667,0.00004378331,0.00001490291,0.0004598079,0.0000148504,0.8893874,0.0000543824,0.00009353118,0.1098579],"study_design_scores_gemma":[0.0006513307,0.0004316154,0.00003018011,0.0002440047,0.00005670115,0.003496195,0.001127384,0.5337344,0.4579483,0.0008831876,0.001199738,0.0001970004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.183036,0.0002837146,0.8152155,0.0002568034,0.0009961452,0.00009519514,0.000002879363,0.00003375565,0.00008011527],"genre_scores_gemma":[0.5710012,0.0000162068,0.4285674,0.00007171512,0.0002808681,5.106912e-7,7.720541e-7,0.00001041585,0.0000508956],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5337195,"threshold_uncertainty_score":0.5240043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08698380811361471,"score_gpt":0.3834031404677182,"score_spread":0.2964193323541035,"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."}}