{"id":"W3118544276","doi":"10.1007/s11517-020-02297-4","title":"Classifying sitting, standing, and walking using plantar force data","year":2021,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Simon Fraser University","funders":"SFU Community Trust Endowment Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Forefoot; Plantar fasciitis; Plantar pressure; Sitting; Linear discriminant analysis; Support vector machine; Computer science; Artificial intelligence; Pressure sensor; Physical medicine and rehabilitation; Heel; Pattern recognition (psychology); Medicine; Engineering; Surgery","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007657968,0.0002998081,0.0004139151,0.00007849659,0.0001558089,0.0001070632,0.0004736329,0.0003418738,0.0003039426],"category_scores_gemma":[0.001118729,0.0002616143,0.00005676294,0.000287976,0.00005921627,0.0001016542,0.001039901,0.0006281204,0.000007968014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006782523,"about_ca_system_score_gemma":0.00003614535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006562897,"about_ca_topic_score_gemma":0.000002107377,"domain_scores_codex":[0.9979964,0.00004709517,0.000429766,0.0005799535,0.0003170872,0.0006297138],"domain_scores_gemma":[0.998656,0.0005975611,0.00004780537,0.0004034556,0.00003144713,0.0002637782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006881706,0.000325312,0.06317886,0.003028472,0.001033383,0.008050974,0.002536833,0.1174776,0.2714736,0.006875443,0.00490175,0.5210489],"study_design_scores_gemma":[0.0002349584,0.00001813587,0.001211828,0.0004228035,0.00001582054,0.0003753418,0.000116815,0.9916371,0.0007270076,0.00008007356,0.004774584,0.0003855104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6743063,0.003353117,0.3199701,0.0000530993,0.0008801559,0.00008775651,0.00009541604,0.0008341927,0.0004198944],"genre_scores_gemma":[0.9731836,0.0003164658,0.02575736,0.00009665069,0.0003739703,0.00000143299,0.0002228411,0.00003832737,0.00000933447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8741595,"threshold_uncertainty_score":0.9999836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06620218467208959,"score_gpt":0.2700752424166854,"score_spread":0.2038730577445958,"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."}}