{"id":"W4283452450","doi":"10.2196/38689","title":"Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"HalTech; University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; AGE-WELL","keywords":"Inertial measurement unit; Random forest; Machine learning; Artificial intelligence; Sitting; Computer science; Hyperparameter; Convolutional neural network; Wearable computer; Physical medicine and rehabilitation; Algorithm; Physical therapy; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.005463369,0.001140979,0.0009240443,0.0009072566,0.0004231728,0.0007996827,0.001234423,0.001593245,0.0009639192],"category_scores_gemma":[0.009430613,0.0004227684,0.0007304483,0.0006262736,0.000508493,0.000701538,0.0007926138,0.001252204,0.0006384325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000749908,"about_ca_system_score_gemma":0.001296624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007959352,"about_ca_topic_score_gemma":0.003661361,"domain_scores_codex":[0.998609,0.0005582385,0.0001333531,0.0003022205,0.000274264,0.0001229613],"domain_scores_gemma":[0.9963355,0.002301511,0.0002168438,0.000238457,0.0008479817,0.00005962744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004975477,0.0006071187,0.0193363,0.0002319841,0.0002598416,0.0001108216,0.00009715139,0.6205025,0.009735841,0.0008616865,0.001347559,0.3464116],"study_design_scores_gemma":[0.00001272229,0.00010261,0.00205458,0.00001533128,0.00001184792,0.00002068714,0.00001137379,0.9944736,0.002878648,0.0002214527,0.0001909186,0.00000610477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2521921,0.001774741,0.74049,0.0003573374,0.00009919808,0.0005698941,0.0002582918,0.002945527,0.001312879],"genre_scores_gemma":[0.6983634,0.0004654878,0.2979813,0.0001431147,0.00003503582,0.0008609411,0.0007676302,0.00006393621,0.00131921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007959352,"threshold_uncertainty_score":0.02889347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0087033884699272,"score_gpt":0.2665430052684892,"score_spread":0.257839616798562,"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."}}