{"id":"W4212903060","doi":"10.3390/automation3010007","title":"A Multipurpose Wearable Sensor-Based System for Weight Training","year":2022,"lang":"en","type":"article","venue":"Automation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Support vector machine; Artificial intelligence; Computer science; Linear discriminant analysis; Activity recognition; Wearable computer; Machine learning; Random forest; Inertial measurement unit; Perceptron; Decision tree; Accelerometer; Multilayer perceptron; Classifier (UML); Pattern recognition (psychology); Artificial neural network","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":[0.0003385477,0.0007571017,0.0007573503,0.000773849,0.0002881275,0.000527112,0.001022156,0.0007414632,0.009334784],"category_scores_gemma":[0.0006888696,0.0002976581,0.0002968274,0.0006980913,0.0001160763,0.0006671759,0.0005918694,0.0004080748,0.004693839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001988814,"about_ca_system_score_gemma":0.0002541889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005825298,"about_ca_topic_score_gemma":0.001196031,"domain_scores_codex":[0.9995552,0.00004878494,0.00003479939,0.0001707574,0.0001614073,0.00002904834],"domain_scores_gemma":[0.9997327,0.00004241401,0.00004477254,0.0000486485,0.0001001245,0.00003134131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001022926,0.0005246662,0.007136492,0.0006926982,0.000136193,0.0005431038,0.0002259975,0.002196454,0.325224,0.001342163,0.02117346,0.6397818],"study_design_scores_gemma":[0.0005199718,0.005602194,0.08865882,0.0004559869,0.0006412518,0.006730276,0.0002543028,0.3371123,0.3144581,0.003445732,0.2416906,0.0004306272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1240764,0.002528769,0.8145395,0.0006086369,0.001400136,0.001145118,0.003160373,0.02835916,0.024182],"genre_scores_gemma":[0.7381908,0.001094061,0.223025,0.001138697,0.0002872752,0.001182782,0.001862807,0.0002806981,0.03293793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009334784,"threshold_uncertainty_score":0.03122795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210137497059739,"score_gpt":0.2548658563034665,"score_spread":0.2127644813328691,"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."}}