{"id":"W2968482654","doi":"10.1109/tnsre.2019.2935765","title":"Sequential Decision Fusion for Environmental Classification in Assistive Walking","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Motion (physics); Fuse (electrical); Computer science; Hidden Markov model; Artificial intelligence; Fusion; Machine learning; Human–computer interaction; Engineering","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":[0.0009919868,0.0007706474,0.000895239,0.0006665683,0.0003784459,0.0004933028,0.0006450096,0.0005461945,0.001063447],"category_scores_gemma":[0.001941697,0.0003222635,0.0007155198,0.0005982912,0.0002967095,0.0009565983,0.0007038618,0.0006236192,0.0002568893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799345,"about_ca_system_score_gemma":0.0004399205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003753202,"about_ca_topic_score_gemma":0.003424762,"domain_scores_codex":[0.9993405,0.0001278971,0.00006027756,0.0001937955,0.0001835204,0.00009405927],"domain_scores_gemma":[0.9994907,0.0001971127,0.00005844188,0.00003391235,0.0001867169,0.00003317989],"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":[0.001130572,0.0003074067,0.00563735,0.000287281,0.0001625815,0.000376429,0.0003327485,0.1885677,0.04482555,0.002956821,0.001765052,0.7536504],"study_design_scores_gemma":[0.00001642495,0.000188775,0.00316274,0.000019926,0.00006182806,0.00008590929,0.00007335209,0.9822282,0.01044513,0.002931965,0.0007593835,0.00002633123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.141906,0.001036207,0.8533925,0.0001904315,0.0002291685,0.00009240245,0.0001745765,0.0007491455,0.002229585],"genre_scores_gemma":[0.917542,0.0003228897,0.08047848,0.00008669317,0.00005793059,0.00004969993,0.0001745468,0.0000244008,0.001263245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003753202,"threshold_uncertainty_score":0.00746274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007497087719861909,"score_gpt":0.2098950825436253,"score_spread":0.2023979948237634,"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."}}