{"id":"W2001710420","doi":"10.1109/memea.2013.6549706","title":"Correcting Smartphone orientation for accelerometer-based analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Accelerometer; Orientation (vector space); Quaternion; Rotation matrix; Computer science; Offset (computer science); Position (finance); Rotation (mathematics); Computer vision; Frame (networking); Acceleration; Gyroscope; Reference frame; Artificial intelligence; Mathematics; Engineering; Physics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000437301,0.00007391886,0.0001087204,0.0002534201,0.00004716392,0.00004867181,0.00006979112,0.00005593378,0.0005028884],"category_scores_gemma":[0.00004531198,0.00006684128,0.00007320021,0.0006949176,0.00001046992,0.0001134185,0.000005859953,0.00003440565,0.00004960387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000031761,"about_ca_system_score_gemma":0.000003502355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004416497,"about_ca_topic_score_gemma":0.00002951373,"domain_scores_codex":[0.999565,0.000003599825,0.0001384986,0.00009394965,0.00005851643,0.000140423],"domain_scores_gemma":[0.999705,0.0000664957,0.00001749074,0.0001206286,0.00007034421,0.00002008567],"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":[0.00001137412,0.00006176137,0.05826017,0.0001707445,0.0008289262,6.286012e-7,0.0004107181,0.6012349,0.01600596,0.001084299,0.01550903,0.3064216],"study_design_scores_gemma":[0.0002717182,0.00002473966,0.006581441,0.000002331009,0.0000833079,2.361218e-7,0.0003432467,0.7723082,0.2197908,0.0001933902,0.0002605904,0.0001400109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3184535,0.000007692559,0.6796014,0.00002404117,0.0001580437,0.000147882,0.000002051216,0.0007289883,0.0008763613],"genre_scores_gemma":[0.9862674,0.000001040946,0.01329671,0.00005386008,0.0000159739,0.0001270504,0.00004480364,0.00001272566,0.0001804335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6678139,"threshold_uncertainty_score":0.5506276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010337970476724,"score_gpt":0.2146711954531793,"score_spread":0.2043332249764553,"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."}}