{"id":"W4382491329","doi":"10.48550/arxiv.2306.15280","title":"Vision-based interface for grasping intention detection and grip selection : towards intuitive upper-limb assistive devices","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Willow Biosciences (Canada)","funders":"","keywords":"Human–computer interaction; Task (project management); Computer science; Interface (matter); Controllability; Movement (music); Object (grammar); Selection (genetic algorithm); Function (biology); User interface; Physical medicine and rehabilitation; Computer vision; Artificial intelligence; Engineering; Medicine; Acoustics","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":[0.0005786027,0.000551196,0.000329504,0.0002492922,0.00009436323,0.0008606846,0.0008735192,0.0008430873,0.003612038],"category_scores_gemma":[0.002061504,0.0002172553,0.0002849069,0.0001341232,0.0003009834,0.0009842395,0.0007804831,0.0005703619,0.0009445718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001310895,"about_ca_system_score_gemma":0.0002382496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003020286,"about_ca_topic_score_gemma":0.0004244725,"domain_scores_codex":[0.9996574,0.00009346131,0.00002449037,0.00007656981,0.0001222806,0.00002578528],"domain_scores_gemma":[0.9995964,0.0001688107,0.00003721529,0.00004067437,0.0001136496,0.00004331119],"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":[0.0006723282,0.0003608298,0.0007964171,0.0006334271,0.00007506291,0.0003358182,0.0004369972,0.003077563,0.5241497,0.006786594,0.005684383,0.456991],"study_design_scores_gemma":[0.0004188669,0.002173298,0.01564125,0.0003813895,0.0002444728,0.002820712,0.0002865652,0.4949517,0.4024587,0.02937131,0.0510399,0.0002117478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02825948,0.0009273007,0.9647209,0.0003147837,0.000123174,0.0001316552,0.00006864461,0.00268723,0.002766739],"genre_scores_gemma":[0.436554,0.000809813,0.5537944,0.0006972887,0.000111234,0.0002023059,0.0001505088,0.0002387092,0.007441638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003612038,"threshold_uncertainty_score":0.01208347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09391359682071443,"score_gpt":0.2515668191967934,"score_spread":0.1576532223760789,"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."}}