{"id":"https://openalex.org/W3094374001","doi":"https://doi.org/10.1145/3379337.3415896","title":"Servo-Gaussian Model to Predict Success Rates in Manual Tracking","display_name":"Servo-Gaussian Model to Predict Success Rates in Manual Tracking","publication_year":2020,"publication_date":"2020-10-16","ids":{"openalex":"https://openalex.org/W3094374001","doi":"https://doi.org/10.1145/3379337.3415896","mag":"3094374001"},"language":"en","primary_location":{"id":"doi:10.1145/3379337.3415896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3379337.3415896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058457491","display_name":"Shota Yamanaka","orcid":"https://orcid.org/0000-0001-9807-120X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shota Yamanaka","raw_affiliation_strings":["Yahoo Japan Corporation, Chiyoda-ku, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Japan Corporation, Chiyoda-ku, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074305558","display_name":"Hiroki Usuba","orcid":"https://orcid.org/0000-0001-7192-7231"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Usuba","raw_affiliation_strings":["Meiji University, Nakano-ku, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University, Nakano-ku, Tokyo, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102111604","display_name":"Haruki Takahashi","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Haruki Takahashi","raw_affiliation_strings":["Meiji University, Nakano-ku, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University, Nakano-ku, Tokyo, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091629034","display_name":"Homei Miyashita","orcid":"https://orcid.org/0000-0001-7533-8709"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Homei Miyashita","raw_affiliation_strings":["Meiji University, Nakano-ku, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University, Nakano-ku, Tokyo, Japan","institution_ids":["https://openalex.org/I16656306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9198,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.86162885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"844","last_page":"857"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10982","display_name":"Motor Control and Adaptation","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10982","display_name":"Motor Control and Adaptation","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12006","display_name":"Ergonomics and Musculoskeletal Disorders","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5961564779281616},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.46849068999290466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43302708864212036},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43049031496047974},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4283739924430847},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.0697784423828125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5961564779281616},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.46849068999290466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43302708864212036},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43049031496047974},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4283739924430847},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0697784423828125},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3379337.3415896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3379337.3415896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W1575989225","https://openalex.org/W1926516617","https://openalex.org/W1973090377","https://openalex.org/W1973624057","https://openalex.org/W1977140659","https://openalex.org/W1978559564","https://openalex.org/W1980860900","https://openalex.org/W1981828094","https://openalex.org/W1982851084","https://openalex.org/W1996884089","https://openalex.org/W2014332669","https://openalex.org/W2017150428","https://openalex.org/W2018631692","https://openalex.org/W2022416142","https://openalex.org/W2027172717","https://openalex.org/W2029047147","https://openalex.org/W2031485125","https://openalex.org/W2031720946","https://openalex.org/W2034718500","https://openalex.org/W2036254707","https://openalex.org/W2043734379","https://openalex.org/W2046807149","https://openalex.org/W2048948826","https://openalex.org/W2050726337","https://openalex.org/W2051869243","https://openalex.org/W2059048668","https://openalex.org/W2061737487","https://openalex.org/W2076680033","https://openalex.org/W2076863986","https://openalex.org/W2082169947","https://openalex.org/W2083004685","https://openalex.org/W2094915802","https://openalex.org/W2098924240","https://openalex.org/W2101576232","https://openalex.org/W2105512500","https://openalex.org/W2109866835","https://openalex.org/W2125239175","https://openalex.org/W2128213565","https://openalex.org/W2131142774","https://openalex.org/W2133028617","https://openalex.org/W2135101375","https://openalex.org/W2153900134","https://openalex.org/W2158391167","https://openalex.org/W2165220806","https://openalex.org/W2168124074","https://openalex.org/W2168443748","https://openalex.org/W2170426794","https://openalex.org/W2179427518","https://openalex.org/W2179931182","https://openalex.org/W2397516279","https://openalex.org/W2404075216","https://openalex.org/W2535420075","https://openalex.org/W2603019185","https://openalex.org/W2611199766","https://openalex.org/W2611792579","https://openalex.org/W2734370323","https://openalex.org/W2766328706","https://openalex.org/W2795496097","https://openalex.org/W2795705486","https://openalex.org/W2901719502","https://openalex.org/W2911456302","https://openalex.org/W2940523683","https://openalex.org/W2940578565","https://openalex.org/W2940881478","https://openalex.org/W2942984216","https://openalex.org/W2944302346","https://openalex.org/W2980458326","https://openalex.org/W2985228903","https://openalex.org/W3000539826","https://openalex.org/W3029223560","https://openalex.org/W3030431617","https://openalex.org/W3030492449","https://openalex.org/W3091169428","https://openalex.org/W3104868866","https://openalex.org/W4210916243","https://openalex.org/W4239127880","https://openalex.org/W4247495437"],"related_works":["https://openalex.org/W3094321810","https://openalex.org/W4287866584","https://openalex.org/W2296101746","https://openalex.org/W3007858788","https://openalex.org/W2475885638","https://openalex.org/W2745238997","https://openalex.org/W2889817876","https://openalex.org/W2353099510","https://openalex.org/W3008330613","https://openalex.org/W1574480537"],"abstract_inverted_index":{"We":[0,31],"propose":[1],"a":[2,27,45,59,72],"Servo-Gaussian":[3],"model":[4,106],"to":[5,18,52],"predict":[6,53],"success":[7,55,69],"rates":[8],"in":[9],"continuous":[10],"manual":[11],"tracking":[12],"tasks.":[13,99],"Two":[14],"tasks":[15],"were":[16],"conducted":[17],"validate":[19],"this":[20],"model:":[21],"path":[22],"steering":[23,92],"and":[24,66,89,93,95],"pursuit":[25,98],"of":[26,81],"1D":[28],"moving":[29],"target.":[30],"hypothesized":[32],"that":[33,103],"(1)":[34],"hand":[35],"movements":[36],"follow":[37],"the":[38,54,64,68,79],"servo-mechanism":[39],"model,":[40],"(2)":[41],"submovement":[42,60],"endpoints":[43],"form":[44],"bivariate":[46],"Gaussian":[47],"distribution,":[48],"thus":[49],"enabling":[50],"us":[51],"rate":[56,70],"at":[57],"which":[58],"endpoint":[61],"falls":[62],"inside":[63],"tolerance,":[65],"(3)":[67],"for":[71,91,97,112],"whole":[73],"trial":[74],"can":[75],"be":[76],"predicted":[77],"if":[78],"number":[80],"submovements":[82],"is":[83],"known.":[84],"The":[85],"cross-validation":[86],"showed":[87],"R^2>0.92":[88],"MAE<4.9%":[90],"R^2>0.95":[94],"MAE<6.5%":[96],"These":[100],"results":[101],"demonstrate":[102],"our":[104],"proposed":[105],"delivers":[107],"high":[108],"prediction":[109],"accuracy":[110],"even":[111],"unknown":[113],"datasets.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}