{"id":"W2743455370","doi":"10.1109/icorr.2017.8009451","title":"Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding","year":2017,"lang":"en","type":"article","venue":"","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Coding (social sciences); Wearable computer; Artificial intelligence; Curse of dimensionality; Machine learning; Neural coding; Robot; Wearable technology; Human–computer interaction; Embedded system","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":[0.0006680501,0.0003774094,0.0003848596,0.0003169203,0.0001868914,0.0008835758,0.0004810686,0.0004393365,0.001593942],"category_scores_gemma":[0.005047464,0.0001499113,0.0003372888,0.0005880828,0.0007371215,0.002018514,0.001095349,0.0007876986,0.0002542657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000272082,"about_ca_system_score_gemma":0.0004402887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001421454,"about_ca_topic_score_gemma":0.001925105,"domain_scores_codex":[0.9995602,0.0001367124,0.0000290342,0.00006676182,0.000164566,0.00004269005],"domain_scores_gemma":[0.9976329,0.001376862,0.0001469316,0.0004922692,0.0002888164,0.00006222269],"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":[0.002285967,0.0002510421,0.0022771,0.000641802,0.0001075199,0.0002519096,0.0008324491,0.1577188,0.1428114,0.03251827,0.0018214,0.6584823],"study_design_scores_gemma":[0.00005861646,0.0008596935,0.002968536,0.00009423601,0.00006049965,0.0003922175,0.0003947969,0.9125485,0.05853651,0.01856649,0.005460996,0.00005879242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2018572,0.0009731586,0.7909974,0.0003006021,0.0001277778,0.00008462063,0.0001038865,0.0004261073,0.005129161],"genre_scores_gemma":[0.826829,0.0006997528,0.1699116,0.0001531068,0.00003066417,0.00008232942,0.0001952835,0.00008260658,0.002015737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001593942,"threshold_uncertainty_score":0.005332291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07856302691274919,"score_gpt":0.3113761961778631,"score_spread":0.2328131692651139,"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."}}