{"id":"W2942343679","doi":"10.2196/14090","title":"A Combination of Indoor Localization and Wearable Sensor–Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Agency for Healthcare Research and Quality; U.S. Department of Health and Human Services","keywords":"Wearable computer; Beacon; Computer science; Wearable technology; Activity tracker; Accelerometer; Activity recognition; Smartwatch; Bluetooth; Population; Artificial intelligence; Human–computer interaction; Wireless; Real-time computing; Medicine; Embedded system; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00169956,0.0008902925,0.0011121,0.0006059224,0.0004387709,0.0008462165,0.0004298156,0.0007206066,0.0009983421],"category_scores_gemma":[0.003022775,0.0003183897,0.001446576,0.0005296733,0.0002645128,0.000684308,0.0006900201,0.0007726656,0.0005613061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003615545,"about_ca_system_score_gemma":0.0003548617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003353627,"about_ca_topic_score_gemma":0.004300531,"domain_scores_codex":[0.9992043,0.0002352807,0.00007469832,0.0002645188,0.0001309805,0.00009018288],"domain_scores_gemma":[0.9986149,0.0001832758,0.0002693438,0.0001618871,0.0005276742,0.0002427616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006018344,0.002636858,0.9654989,0.0001632736,0.001222531,0.0001596649,0.000472875,0.0002509233,0.001649145,0.00002498939,0.0005317855,0.0213706],"study_design_scores_gemma":[0.0002090457,0.008204229,0.9884505,0.00002859432,0.0007255213,0.0002210295,0.0003529786,0.0007785849,0.0004155,0.00004058739,0.0005472815,0.00002596506],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980191,0.0004581103,0.0004663505,0.00004267031,0.00002443505,0.0001086789,0.0004930308,0.00001351767,0.0003741464],"genre_scores_gemma":[0.9972972,0.0002680488,0.0006104223,0.0000598156,0.0000617197,0.0001536015,0.001158323,0.000005916771,0.0003848008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003353627,"threshold_uncertainty_score":0.008988261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05686620121311808,"score_gpt":0.3529818957211854,"score_spread":0.2961156945080674,"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."}}