{"id":"W2294136539","doi":"","title":"Towards improved performance and compliance in healthcare using wearables and bluetooth technologies","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wearable computer; Bluetooth; Smartwatch; Computer science; Wearable technology; Health care; Domain (mathematical analysis); Corporate governance; Compliance (psychology); Human–computer interaction; Data science; Embedded system; Risk analysis (engineering); Wireless; Business; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005759698,0.0009175871,0.0007970751,0.001220661,0.0007439695,0.003880009,0.001028114,0.001416995,0.001230429],"category_scores_gemma":[0.01422303,0.0003543566,0.0003730528,0.001830746,0.001142477,0.004091172,0.003140275,0.001446981,0.0008916822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006636695,"about_ca_system_score_gemma":0.001038586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009349929,"about_ca_topic_score_gemma":0.001225542,"domain_scores_codex":[0.9935114,0.003150256,0.0004901005,0.0009302268,0.001566704,0.0003514352],"domain_scores_gemma":[0.9894952,0.003856283,0.002329089,0.001928714,0.002030818,0.000359829],"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":[0.0003986929,0.000811235,0.08337205,0.0008988519,0.0001844356,0.0002821433,0.003462374,0.03614788,0.04255779,0.03061883,0.007775207,0.7934906],"study_design_scores_gemma":[0.0001876513,0.005818821,0.2322027,0.002443536,0.0005354554,0.002015958,0.01281399,0.3524706,0.1428071,0.1363573,0.111671,0.0006759662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2560292,0.00378656,0.7027784,0.009771255,0.0003533816,0.0002436189,0.00035123,0.00291255,0.0237738],"genre_scores_gemma":[0.8811857,0.001478645,0.1141672,0.0006524313,0.0002217955,0.000115659,0.0002135576,0.0001433585,0.001821702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005759698,"threshold_uncertainty_score":0.0304606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02597620681379306,"score_gpt":0.2307053926303006,"score_spread":0.2047291858165076,"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."}}