{"id":"W3094100206","doi":"10.3390/electronics9111753","title":"Real-Time Remote Health Monitoring System Driven by 5G MEC-IoT","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Cloud computing; Telemedicine; Computer science; Edge computing; Internet of Things; Big data; Low latency (capital markets); Enhanced Data Rates for GSM Evolution; Health care; Real-time computing; Computer network; Telecommunications; Computer security; Data mining; Operating system","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":[0.0002154388,0.0003460752,0.0005509444,0.000353952,0.0003560472,0.000373503,0.0004928772,0.0005328163,0.002802658],"category_scores_gemma":[0.0002550084,0.0001242601,0.0002311871,0.0002039856,0.0001303046,0.0004386308,0.0004358856,0.0002480714,0.0005066958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000192673,"about_ca_system_score_gemma":0.0001675136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00070084,"about_ca_topic_score_gemma":0.0008585754,"domain_scores_codex":[0.9997771,0.00003703767,0.0000194062,0.00006963625,0.00006401734,0.00003280837],"domain_scores_gemma":[0.9998896,0.00002225491,0.00001232484,0.00001626938,0.00004092017,0.00001864155],"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":[0.00318097,0.00092808,0.03770838,0.0007947561,0.0003043139,0.007551047,0.0008526123,0.04846084,0.3770216,0.0093823,0.04662205,0.467193],"study_design_scores_gemma":[0.0003831187,0.001258017,0.02784203,0.00009024923,0.0002408763,0.004027884,0.0002215107,0.8797771,0.06305227,0.002880391,0.02010443,0.0001221608],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5104181,0.002085306,0.4246756,0.00229467,0.0009621283,0.0005474994,0.000985131,0.01696682,0.04106482],"genre_scores_gemma":[0.9814052,0.0001759387,0.01461645,0.0003591882,0.00007318321,0.00007157052,0.0002302593,0.00002805515,0.003040074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002802658,"threshold_uncertainty_score":0.00937587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338251606914824,"score_gpt":0.2323399845372449,"score_spread":0.2189574684680966,"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."}}