{"id":"W4225979779","doi":"10.7717/peerj.12752","title":"Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution","year":2022,"lang":"en","type":"article","venue":"PeerJ","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Ground reaction force; Accelerometer; Artificial neural network; Kinematics; Waveform; Simulation; Treadmill; Wearable computer; Mean squared error; Computer science; Range (aeronautics); Mathematics; Engineering; Artificial intelligence; Statistics; Physics; Medicine; Physical therapy; 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":[0.0008840624,0.0008961086,0.0006359263,0.0003429632,0.0002725033,0.0005486743,0.000766958,0.0007847882,0.0008168314],"category_scores_gemma":[0.002833828,0.0004665426,0.0006184201,0.0002687587,0.0002432526,0.0005022816,0.0004037925,0.0008775352,0.0002120072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005796175,"about_ca_system_score_gemma":0.0007692922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01838167,"about_ca_topic_score_gemma":0.01468457,"domain_scores_codex":[0.9997721,0.00005118641,0.00001818343,0.00008544556,0.00003456256,0.00003841209],"domain_scores_gemma":[0.9992819,0.0003780802,0.00009128809,0.00003196852,0.0001856326,0.00003119392],"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":[0.0001948651,0.0001475374,0.008809659,0.0000396632,0.000108825,0.0001495504,0.00006629192,0.9229275,0.003662392,0.000316322,0.0006086837,0.06296881],"study_design_scores_gemma":[0.000002859646,0.0000189207,0.0007100743,0.000002384694,0.000006334668,0.000005145336,0.000004361271,0.998858,0.0002395384,0.000128319,0.00002162399,0.000002535171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5128636,0.0005370768,0.483333,0.0004927186,0.00008177742,0.00009563512,0.0003168971,0.0007772467,0.001502091],"genre_scores_gemma":[0.9683633,0.0001232041,0.02993299,0.00006152251,0.00002555552,0.00007164619,0.0003596969,0.00002136886,0.001040812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01838167,"threshold_uncertainty_score":0.03654933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757434728552766,"score_gpt":0.2058735512602904,"score_spread":0.1882992039747627,"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."}}