{"id":"W4395081390","doi":"10.18280/ria.380202","title":"Smart Wheelchair Localization and Navigation Based on Multi-Sensor Data Fusion Using Hybrid-Filter Method (HF)","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sensor fusion; Wheelchair; Computer science; Filter (signal processing); Fusion; Artificial intelligence; Real-time computing; Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003603497,0.0004377913,0.0005253684,0.0004776997,0.0002606468,0.0004569734,0.0003519709,0.0006997499,0.0006512055],"category_scores_gemma":[0.0006053968,0.0001686077,0.0005501101,0.0004909765,0.0002159867,0.0007619776,0.0004307917,0.0003182586,0.000211541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462812,"about_ca_system_score_gemma":0.0004747228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005145583,"about_ca_topic_score_gemma":0.003673383,"domain_scores_codex":[0.9997738,0.00004517666,0.00001462303,0.0000622088,0.00008217077,0.00002198992],"domain_scores_gemma":[0.9998568,0.0000517959,0.00002252549,0.00001442492,0.00004867241,0.000005817938],"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":[0.0002480065,0.0001063135,0.004174575,0.0003676451,0.0002498288,0.00025959,0.0002507053,0.3840265,0.0546139,0.004802701,0.00149557,0.5494047],"study_design_scores_gemma":[0.00001168271,0.0001094271,0.001694299,0.00001599172,0.00003316432,0.00009644886,0.0000293495,0.9895291,0.006092981,0.0009832099,0.001378724,0.00002559321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01523845,0.0001998371,0.9835781,0.00004548658,0.00004167305,0.0000177485,0.00002047236,0.0002202592,0.0006379709],"genre_scores_gemma":[0.7406073,0.0006778176,0.2560702,0.00007133279,0.0000598385,0.0001153065,0.0001205131,0.00002467331,0.002253059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005145583,"threshold_uncertainty_score":0.01023126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127862249959058,"score_gpt":0.3350263435695806,"score_spread":0.2222401185736748,"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."}}