{"id":"W2532464425","doi":"10.1109/embc.2016.7591531","title":"Compressive sensing of foot-gait signals by enhancing group block-sparse structure on the first-order difference","year":2016,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Block (permutation group theory); Compressed sensing; Gait; Computer science; Regularization (linguistics); SIGNAL (programming language); Algorithm; Group structure; Minification; Norm (philosophy); Artificial intelligence; Pattern recognition (psychology); Mathematics; Physical medicine and rehabilitation; Medicine; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003752196,0.0004014785,0.0003907258,0.0003844007,0.0001689794,0.0002883012,0.0004638017,0.0003904851,0.0008161418],"category_scores_gemma":[0.001500946,0.00016463,0.0002827211,0.0005004251,0.0003658642,0.0006410116,0.0005196814,0.0005204096,0.0002431246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001864751,"about_ca_system_score_gemma":0.0003936542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008140862,"about_ca_topic_score_gemma":0.001386037,"domain_scores_codex":[0.9997024,0.00006468797,0.00001093159,0.00003152009,0.0001733238,0.0000171809],"domain_scores_gemma":[0.9995396,0.0002230164,0.00005425846,0.00005208445,0.0001120271,0.00001912405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003632131,0.0001384439,0.001226064,0.000302409,0.00006395672,0.0001517677,0.000221545,0.145469,0.3687145,0.02424023,0.002163804,0.4569452],"study_design_scores_gemma":[0.00001889529,0.0002393283,0.000581027,0.00001133914,0.00001347995,0.0002050561,0.00002040934,0.9441183,0.0491083,0.002925993,0.002739363,0.00001858954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02544097,0.0001417282,0.973225,0.0001145161,0.00003808478,0.000026994,0.0000282861,0.0001261134,0.00085832],"genre_scores_gemma":[0.3020078,0.0003971548,0.6950011,0.0001129418,0.00007789255,0.00007490746,0.0001601626,0.0000395764,0.002128453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008161418,"threshold_uncertainty_score":0.00273025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109877456626113,"score_gpt":0.2031270035094011,"score_spread":0.1921392578467898,"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."}}