{"id":"W2084223998","doi":"10.1016/s1050-6411(00)00030-4","title":"Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution","year":2000,"lang":"en","type":"article","venue":"Journal of Electromyography and Kinesiology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":356,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Time–frequency analysis; Intensity (physics); SIGNAL (programming language); Wavelet; Frequency analysis; Computer science; Instantaneous phase; Signal processing; Acoustics; Spectral density; Power (physics); Filter (signal processing); Mathematics; Artificial intelligence; Algorithm; Computer vision; Telecommunications; Physics; Optics","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":[0.0007014287,0.0003133385,0.0002342948,0.0004999039,0.0001477571,0.0005241447,0.0002820413,0.0003291674,0.00122318],"category_scores_gemma":[0.002228524,0.0001352167,0.0003605761,0.000946732,0.0003455461,0.0005888739,0.0003574217,0.0005997713,0.0003664311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000126592,"about_ca_system_score_gemma":0.0002171863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003203731,"about_ca_topic_score_gemma":0.0002444119,"domain_scores_codex":[0.9998505,0.00004405066,0.00001088299,0.00002368262,0.00004823382,0.00002265344],"domain_scores_gemma":[0.9994254,0.0002858333,0.00004934998,0.00007599493,0.0001262567,0.00003719104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008488841,0.0001161489,0.001955978,0.0003475947,0.00005836101,0.0002286498,0.000416427,0.02196088,0.5675191,0.02793732,0.0008880201,0.3777225],"study_design_scores_gemma":[0.00006916965,0.0005745803,0.02345233,0.0000853478,0.0001327729,0.0009251112,0.0002514089,0.806568,0.141612,0.01984701,0.006412087,0.00007013038],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1176316,0.0002811137,0.8807201,0.00009490957,0.00003586496,0.00003381563,0.00007557965,0.000120969,0.00100602],"genre_scores_gemma":[0.6118811,0.001238042,0.3840691,0.00003921132,0.00009675747,0.00009098231,0.0002758791,0.0001735942,0.002135291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00122318,"threshold_uncertainty_score":0.004091918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005187921666688365,"score_gpt":0.1941724184465717,"score_spread":0.1889844967798833,"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."}}