{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001299166,0.0001796884,0.0003942463,0.0002821043,0.0001960611,0.00008324245,0.0001101325,0.00007571101,0.000002247133],"category_scores_gemma":[0.0003233709,0.0001818304,0.00003268919,0.0006499944,0.00002772709,0.0007254669,0.00007336705,0.0001643686,0.00001441946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460197,"about_ca_system_score_gemma":0.0002953064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002502886,"about_ca_topic_score_gemma":0.0001097322,"domain_scores_codex":[0.9972436,0.000891729,0.0004606525,0.0006449306,0.0004726793,0.0002863857],"domain_scores_gemma":[0.9969689,0.001439504,0.0004452022,0.0003562655,0.0005300991,0.0002600931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001903281,0.01217656,0.4329102,0.004992657,0.00004837944,0.00000276024,0.01793939,0.0001794178,0.000192213,0.0002147362,0.0005980919,0.5288423],"study_design_scores_gemma":[0.009621985,0.006500577,0.861585,0.0005269447,0.00003080265,0.000002716482,0.001052987,0.1198062,0.0002310254,0.00023165,0.00006903105,0.0003410088],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536645,0.000008530676,0.04190437,0.001542292,0.0001945268,0.00251229,0.0000417614,0.00007817082,0.00005353977],"genre_scores_gemma":[0.9982061,0.000007476491,0.001256951,0.0003302951,0.00003496378,0.0000945777,0.00003643793,0.0000139627,0.00001916736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5285013,"threshold_uncertainty_score":0.7414828,"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."}}