{"id":"W4296835510","doi":"10.3389/frobt.2022.948238","title":"Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification","year":2022,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GRASP; Computer science; Prosthetic hand; Artificial intelligence; Computer vision; Electromyography; Feature (linguistics); Human–computer interaction; Pattern recognition (psychology); Physical medicine and rehabilitation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151838,0.0006509424,0.0005999989,0.0008083974,0.0001370353,0.0004179245,0.0003008403,0.0007367332,0.001233032],"category_scores_gemma":[0.002542447,0.0001595357,0.0003826246,0.0006718218,0.0002876017,0.0004503115,0.0003813202,0.0002739721,0.0003920442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001414884,"about_ca_system_score_gemma":0.0001889929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001725099,"about_ca_topic_score_gemma":0.001735011,"domain_scores_codex":[0.9994556,0.0001666653,0.00004945861,0.0001039132,0.0001533677,0.00007097697],"domain_scores_gemma":[0.9988977,0.0006083975,0.00006099549,0.0001032943,0.0002564387,0.00007318775],"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":[0.001631821,0.001050929,0.01763598,0.0005224774,0.0002238623,0.0003191703,0.0002232501,0.01576111,0.2020385,0.000196162,0.0008641795,0.7595326],"study_design_scores_gemma":[0.0002073201,0.006709749,0.1972034,0.0001512938,0.0005209148,0.001595703,0.0004565236,0.6144707,0.1739482,0.001513957,0.003100183,0.00012215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8877997,0.002603216,0.105896,0.0001852416,0.0001179185,0.0001346753,0.0002123737,0.0008292902,0.002221634],"genre_scores_gemma":[0.9622114,0.0005592281,0.03545721,0.00006465465,0.00004060449,0.00003292268,0.0002073168,0.00003789472,0.001388788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001725099,"threshold_uncertainty_score":0.006091595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005790928197324872,"score_gpt":0.2036947604860754,"score_spread":0.1979038322887506,"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."}}