{"id":"W227238283","doi":"10.1016/j.bspc.2015.04.012","title":"Robust compressive sensing algorithm for wireless surface electromyography applications","year":2015,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Compressed sensing; Wireless; Electromyography; Algorithm; Artificial intelligence; Telecommunications; Physical medicine and rehabilitation; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001550356,0.0001586292,0.0002472032,0.00004568787,0.0001439828,0.00008686753,0.00007222365,0.00009920973,0.000002044737],"category_scores_gemma":[0.000009781607,0.000140987,0.0000343942,0.0001549228,0.0001315709,0.00009215208,0.000008831171,0.00009096979,0.000001330458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002197002,"about_ca_system_score_gemma":0.00003297357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000834376,"about_ca_topic_score_gemma":4.751204e-7,"domain_scores_codex":[0.9990965,0.00002210671,0.0002121273,0.0002031366,0.000156654,0.0003095418],"domain_scores_gemma":[0.9994254,0.00009524019,0.00004713563,0.00006557277,0.0001085492,0.0002581643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003015671,0.00002920725,0.000008482912,0.0001241868,0.00004871054,0.000003919909,0.00008250391,0.01026135,0.1046615,0.0000451028,0.0002871341,0.8844177],"study_design_scores_gemma":[0.001697782,0.0000839352,0.00001004562,0.00007306254,0.00005136431,0.00002319002,0.0001030105,0.9794978,0.00490522,0.001142663,0.01213978,0.0002721124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01049291,0.001276236,0.9874176,0.00006426861,0.00007276121,0.0002019893,0.00003397475,0.0003306726,0.0001095459],"genre_scores_gemma":[0.961221,0.0000154674,0.03817503,0.00007872884,0.0003774628,0.00003562249,0.00003768743,0.00003352606,0.00002548558],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9692365,"threshold_uncertainty_score":0.5749284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654372350410769,"score_gpt":0.2243184016821482,"score_spread":0.2077746781780405,"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."}}