{"id":"W2344177016","doi":"10.1109/jstsp.2016.2530632","title":"Characterization of Upper-Limb Pathological Tremors: Application to Design of an Augmented Haptic Rehabilitation System","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; London Health Sciences Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Haptic technology; Computer science; Rehabilitation; Filter (signal processing); Adaptive filter; Simulation; Artificial intelligence; Physical medicine and rehabilitation; Computer vision; Medicine; Physical therapy; Algorithm","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":[0.0002954657,0.000348022,0.0002409852,0.0003817462,0.0001297431,0.0003561956,0.0002618144,0.0005530234,0.0006864455],"category_scores_gemma":[0.0008920208,0.0001158818,0.0002420271,0.0002078052,0.0002092086,0.0002817916,0.0001497347,0.0001889167,0.000184128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405648,"about_ca_system_score_gemma":0.0002452701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007710404,"about_ca_topic_score_gemma":0.001029672,"domain_scores_codex":[0.9998141,0.00003195082,0.00001882091,0.00003892988,0.00008239584,0.00001383685],"domain_scores_gemma":[0.9995734,0.0001705593,0.00007378421,0.00004532477,0.0001220246,0.00001497337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003925074,0.0001048388,0.003536238,0.000317309,0.00004867449,0.0002958126,0.0002119645,0.04042757,0.6371467,0.0009759169,0.0003725277,0.3161699],"study_design_scores_gemma":[0.00005118535,0.001247582,0.02043285,0.00004059882,0.0001327461,0.001494921,0.0001238006,0.7218086,0.2482556,0.0009932125,0.0053529,0.00006589336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09160415,0.000353623,0.9064526,0.00007913566,0.00002401502,0.0000632521,0.00003926963,0.0004472184,0.0009367393],"genre_scores_gemma":[0.7405967,0.0003472661,0.2574402,0.00006294443,0.00003099884,0.00007804003,0.00008348641,0.00002904935,0.001331374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007710404,"threshold_uncertainty_score":0.002296388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922829987823827,"score_gpt":0.2752100119564936,"score_spread":0.2559817120782553,"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."}}