{"id":"W4320073485","doi":"10.2196/42714","title":"Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study","year":2023,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; University of South Carolina","keywords":"Accelerometer; Smartwatch; Gesture; Human–computer interaction; Computer science; Wearable computer; Artificial intelligence; Embedded system; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003971008,0.0001724318,0.0002611482,0.0005524802,0.0005023435,0.00005589675,0.0002549339,0.0001113198,0.0003752397],"category_scores_gemma":[0.000401546,0.0001495226,0.00001956083,0.001135421,0.00007619796,0.0001589581,0.0002965385,0.0004180089,0.00004289567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007210039,"about_ca_system_score_gemma":0.0000542249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001440862,"about_ca_topic_score_gemma":0.0000362861,"domain_scores_codex":[0.9984071,0.00007713035,0.0003568199,0.0004736061,0.0004376121,0.0002477862],"domain_scores_gemma":[0.9989281,0.00007634045,0.0003149606,0.000492718,0.00008160764,0.0001062784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000978621,0.0000939953,0.9624954,0.00004298177,0.0000769664,0.0000076889,0.001093914,0.000001707808,0.03034894,0.000003145241,0.0001375614,0.005687911],"study_design_scores_gemma":[0.001393528,0.0003664104,0.981997,0.0001790139,0.0001156644,0.00002058602,0.00380416,0.005542882,0.004916574,0.00008014619,0.001350717,0.0002333174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980279,0.00003415061,0.0001888711,0.0002889885,0.00008914528,0.0008536829,0.000004578037,0.0004047941,0.0001078842],"genre_scores_gemma":[0.99863,0.00001275092,0.0003301082,0.00003394196,0.0000580358,0.00006629041,0.0003301925,0.00002593841,0.0005127194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02543236,"threshold_uncertainty_score":0.6097356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1876953373320538,"score_gpt":0.4074901612544152,"score_spread":0.2197948239223614,"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."}}