{"id":"W2796184738","doi":"10.1145/3173574.3173693","title":"Understanding Older Users' Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Government of Canada; AGE-WELL","keywords":"Wearable computer; Computer science; USable; Interface (matter); Wearable technology; Human–computer interaction; Software deployment; Field (mathematics); Multimedia; Embedded system; Software engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005748619,0.0001443409,0.0002478723,0.0001336716,0.0001558157,0.0001888454,0.0004990058,0.00006387176,0.0001010345],"category_scores_gemma":[0.00006219682,0.0001299265,0.00009351134,0.0002721345,0.00008864718,0.0006865088,0.0001058707,0.00008909314,0.00003073623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759898,"about_ca_system_score_gemma":0.0001203275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001987482,"about_ca_topic_score_gemma":0.0003386143,"domain_scores_codex":[0.9986249,0.0001204794,0.0003084594,0.0004072,0.000272638,0.0002663004],"domain_scores_gemma":[0.9983286,0.0005634375,0.0002644678,0.0004817387,0.00029535,0.00006639816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006202632,0.002999666,0.4558217,0.001591948,0.001846018,0.00001916295,0.0154024,0.001241452,0.08144309,0.1010551,0.07110778,0.2668514],"study_design_scores_gemma":[0.005845847,0.001673245,0.01442625,0.0008202395,0.00007757278,0.00001373421,0.005860263,0.7253283,0.2301654,0.008093483,0.006431703,0.001264013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06529347,0.0000144272,0.9274071,0.0004869346,0.0004911079,0.0004539558,0.00001335048,0.0001465524,0.005693117],"genre_scores_gemma":[0.9387662,0.000003220263,0.06045482,0.0001716755,0.00005608479,0.00003059204,8.198827e-7,0.00001101185,0.0005055709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8734728,"threshold_uncertainty_score":0.529825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153299718038535,"score_gpt":0.3280639400659092,"score_spread":0.2127339682620557,"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."}}