{"id":"W4318605464","doi":"10.1109/aivr56993.2022.00025","title":"Capture and Recognition of Bead Weaving Activities using Hand Skeletal Data and an LSTM Deep Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Craft; Weaving; Workflow; Artificial intelligence; Gesture; Context (archaeology); Augmented reality; Artificial neural network; Deep learning; Process (computing); Human–computer interaction; Machine learning; Engineering; Database; Visual arts","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004673573,0.000111666,0.0001811396,0.00007334736,0.000365778,0.0001976439,0.0003241973,0.00003700658,0.00002346611],"category_scores_gemma":[0.00001447743,0.0001055673,0.0000161563,0.0002140498,0.00006839022,0.001102624,0.0007818972,0.0001484173,3.396795e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001342531,"about_ca_system_score_gemma":0.00002506298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001737915,"about_ca_topic_score_gemma":0.0001790548,"domain_scores_codex":[0.9987808,0.0002284687,0.0001903892,0.0003926919,0.0002199225,0.0001876538],"domain_scores_gemma":[0.999285,0.0001139144,0.000117309,0.0003741298,0.00003516082,0.0000744651],"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":[0.00006297904,0.000220409,0.01049342,0.0002525235,0.0001592509,0.0000751553,0.01474497,0.002334692,0.01679257,0.0008278098,0.0007091834,0.9533271],"study_design_scores_gemma":[0.0007230811,0.0002481457,0.002191679,0.00005690635,0.00004564528,0.0008558379,0.003140841,0.9890014,0.0007691958,0.001531485,0.0009861467,0.0004496382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8703216,0.0008379194,0.1276694,0.000103579,0.0004075492,0.0001924049,0.00006287648,0.00005928625,0.0003453776],"genre_scores_gemma":[0.9878924,0.000008860791,0.01170591,0.0001358871,0.0001554067,0.000005834318,0.00006069404,0.000008341991,0.000026645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9866667,"threshold_uncertainty_score":0.4304911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05590826842443038,"score_gpt":0.2803613952621253,"score_spread":0.2244531268376949,"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."}}