{"id":"W2593796416","doi":"10.3390/s17030529","title":"A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Activity recognition; Accelerometer; Computer science; Artificial intelligence; Machine learning; Support vector machine; Principal component analysis; Feature (linguistics); Motion (physics); Wearable technology; Dimension (graph theory); Pattern recognition (psychology); Human–computer interaction; Data mining","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001331626,0.00102785,0.0007637743,0.001988065,0.0003406735,0.0009420836,0.0004653273,0.0007632184,0.001097418],"category_scores_gemma":[0.004414701,0.0003550752,0.0009597689,0.002847292,0.000358928,0.001366121,0.0004614233,0.0008142134,0.0006019888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003003322,"about_ca_system_score_gemma":0.0004582389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001404127,"about_ca_topic_score_gemma":0.001486927,"domain_scores_codex":[0.9984688,0.0003710449,0.0001363504,0.0003323294,0.0006134635,0.00007792279],"domain_scores_gemma":[0.9970145,0.00141653,0.0002141192,0.0003564128,0.0009294105,0.00006897862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002752499,0.0002405633,0.01531519,0.001560559,0.000331814,0.0002595103,0.0001616221,0.0225349,0.02182713,0.005573528,0.008428435,0.9234915],"study_design_scores_gemma":[0.00004653204,0.002866527,0.3162208,0.002603644,0.001149382,0.004119615,0.0008041569,0.3632089,0.08945254,0.01992451,0.1992235,0.000379988],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2318328,0.1426459,0.5951225,0.003841049,0.001123973,0.0005193923,0.005327209,0.001330606,0.01825666],"genre_scores_gemma":[0.7383709,0.0962373,0.1450219,0.0009604073,0.001329101,0.0004301504,0.01073677,0.0001625425,0.006750813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001988065,"threshold_uncertainty_score":0.007042408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08897369318164437,"score_gpt":0.3240306732050064,"score_spread":0.235056980023362,"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."}}