{"id":"W2888460178","doi":"10.3390/s18082725","title":"Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quaternion; Wearable computer; Orientation (vector space); Computer science; Artificial intelligence; Computer vision; Frame (networking); Units of measurement; Wireless sensor network; Mathematics; Embedded system","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":[0.000278444,0.0002074382,0.0002184204,0.0003130866,0.0004932288,0.0003012863,0.000262127,0.0001005729,0.0001718836],"category_scores_gemma":[0.0001414184,0.0002230712,0.00008905328,0.0008436071,0.00006430228,0.001206888,0.00007197179,0.0001646013,0.0006123534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001212946,"about_ca_system_score_gemma":0.0000973534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001040294,"about_ca_topic_score_gemma":0.0002799841,"domain_scores_codex":[0.9978146,0.0004205219,0.0003799197,0.0005337375,0.0004966632,0.0003545333],"domain_scores_gemma":[0.998458,0.00020518,0.0001813104,0.0004151945,0.0005379665,0.0002023632],"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":[0.0004989834,0.001284879,0.002195997,0.0001015436,0.0004099784,0.00002717186,0.02020865,0.001866671,0.7681729,0.0009948788,0.003722726,0.2005156],"study_design_scores_gemma":[0.002298138,0.0003951303,0.01094279,0.0002688254,0.00007763979,0.0001964995,0.001010672,0.7447801,0.2350066,0.0010693,0.002850117,0.001104089],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5576863,0.000001444772,0.4401894,0.0006402747,0.0004162905,0.0003886962,0.00009566453,0.0001465302,0.0004354111],"genre_scores_gemma":[0.9935449,0.000002606884,0.005592467,0.0001328769,0.0001957611,0.00004433488,0.00008035498,0.00001937224,0.000387369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7429135,"threshold_uncertainty_score":0.9096581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06201171240997967,"score_gpt":0.3019740769788967,"score_spread":0.2399623645689171,"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."}}