{"id":"W2294346212","doi":"10.1109/embc.2015.7318409","title":"Fitness activity classification by using multiclass support vector machines on head-worn sensors","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Accelerometer; Support vector machine; Multiclass classification; Computer science; Stairs; Head (geology); Wearable computer; Artificial intelligence; Pressure sensor; Activity recognition; Global Positioning System; Computer vision; Pattern recognition (psychology); Engineering; Embedded system; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004643761,0.0008057803,0.0006689414,0.0008166101,0.0001686964,0.0004980177,0.0004206352,0.0004317588,0.0008333558],"category_scores_gemma":[0.0017654,0.0001337836,0.0004143167,0.0006780925,0.0001129314,0.0005473298,0.0003367245,0.0005018112,0.0006389463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921016,"about_ca_system_score_gemma":0.0002115007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956412,"about_ca_topic_score_gemma":0.002210265,"domain_scores_codex":[0.9994623,0.0001383417,0.00005236505,0.0001606711,0.0001224079,0.00006387669],"domain_scores_gemma":[0.9993091,0.0002902673,0.00009081716,0.00007910674,0.0002012236,0.00002957509],"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":[0.000302751,0.0003725769,0.01151526,0.0001175334,0.0001349263,0.00008986163,0.00005955463,0.03429796,0.03259056,0.0002484297,0.00154171,0.9187289],"study_design_scores_gemma":[0.00002004317,0.0003806678,0.01748113,0.0000286892,0.00004840848,0.00009628929,0.00007127076,0.9632816,0.01690957,0.0006753456,0.0009814168,0.00002547573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4555736,0.001084804,0.5370455,0.0002303018,0.0002388345,0.0001310631,0.0006055421,0.002940023,0.002150321],"genre_scores_gemma":[0.9147526,0.0002424219,0.08290569,0.00005908699,0.00006126569,0.00008839557,0.00044069,0.00002374393,0.001426153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001956412,"threshold_uncertainty_score":0.003890038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1461313565761273,"score_gpt":0.3413219706166594,"score_spread":0.1951906140405321,"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."}}