{"id":"W4286111648","doi":"10.3390/bios12070549","title":"Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition","year":2022,"lang":"en","type":"article","venue":"Biosensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Classifier (UML); Orientation (vector space); Accelerometer; Deep learning; Heuristic; Invariant (physics); Computer vision; Precision and recall; Mathematics","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":[0.0004111804,0.0006917043,0.0004731216,0.0002978417,0.000106445,0.0003680885,0.0005066618,0.000405383,0.0007857928],"category_scores_gemma":[0.001114705,0.000197864,0.0003280343,0.0003429345,0.00017119,0.0008237267,0.0003928959,0.000507319,0.0002338456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337436,"about_ca_system_score_gemma":0.0003729922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003750532,"about_ca_topic_score_gemma":0.005334791,"domain_scores_codex":[0.9998178,0.00003162315,0.00001196668,0.00006885073,0.00003351535,0.00003632209],"domain_scores_gemma":[0.9998106,0.00008496852,0.00002841866,0.00001980838,0.00004131193,0.0000149098],"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":[0.0008706458,0.0003491266,0.009106452,0.0002666096,0.0001749603,0.0002503908,0.000177664,0.166849,0.1069544,0.001683709,0.002408442,0.7109086],"study_design_scores_gemma":[0.00001370816,0.0002076392,0.004122978,0.00001411062,0.00004605229,0.00006501596,0.00004023012,0.9787065,0.01515654,0.001027111,0.0005880659,0.00001210275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5955771,0.003322084,0.3956721,0.0003364315,0.0002135526,0.00007145451,0.0005098996,0.001637545,0.002659815],"genre_scores_gemma":[0.9553331,0.0004053671,0.04290692,0.0000857643,0.00002508413,0.0000427683,0.0003107826,0.00003023154,0.0008599788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003750532,"threshold_uncertainty_score":0.007457376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09954009723169419,"score_gpt":0.2577299334416577,"score_spread":0.1581898362099635,"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."}}