{"id":"W2072404679","doi":"10.1016/j.procs.2013.06.061","title":"Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Accelerometer; Gesture; Tracking (education); Mobile phone; Computer vision; Artificial intelligence; Movement (music); Gesture recognition; Foot (prosody); Position (finance); Phone; Human–computer interaction; Acoustics; Telecommunications","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":[0.0003903314,0.0008497285,0.0005591169,0.0008295212,0.0002409939,0.0007015457,0.0005573242,0.001040072,0.001789953],"category_scores_gemma":[0.0008261041,0.0004021717,0.0003736943,0.0007861452,0.0002336797,0.0006546751,0.0004066409,0.0003342866,0.00147836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764225,"about_ca_system_score_gemma":0.0002185611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222484,"about_ca_topic_score_gemma":0.002391388,"domain_scores_codex":[0.9994179,0.0001277078,0.00004086874,0.0001445938,0.0002244595,0.00004445467],"domain_scores_gemma":[0.9995486,0.0001492889,0.00004577794,0.00006847394,0.0001617713,0.00002605415],"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":[0.0003313936,0.00009353412,0.006203874,0.0006091921,0.00007351331,0.000275161,0.000191099,0.001253783,0.3823259,0.0007789804,0.001668594,0.6061949],"study_design_scores_gemma":[0.0002340595,0.003664945,0.1670644,0.0007786962,0.0005614121,0.009140667,0.0006981518,0.1845952,0.5460433,0.004061399,0.08269927,0.0004586029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08167963,0.004848331,0.9058662,0.0001855339,0.0002604351,0.0001921442,0.0003269033,0.002303763,0.004337143],"genre_scores_gemma":[0.5375093,0.004248351,0.4516758,0.000252339,0.0001354728,0.0002486499,0.0004492144,0.00009802722,0.005382921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001789953,"threshold_uncertainty_score":0.005988002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06685342729545717,"score_gpt":0.2667338013985525,"score_spread":0.1998803741030953,"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."}}