{"id":"https://openalex.org/W4311413016","doi":"https://doi.org/10.1109/sensors52175.2022.9967286","title":"An Indirect Method of Brushing Force Detection with Five Force Sensors and RF Algorithm","display_name":"An Indirect Method of Brushing Force Detection with Five Force Sensors and RF Algorithm","publication_year":2022,"publication_date":"2022-10-30","ids":{"openalex":"https://openalex.org/W4311413016","doi":"https://doi.org/10.1109/sensors52175.2022.9967286"},"language":"en","primary_location":{"id":"doi:10.1109/sensors52175.2022.9967286","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sensors52175.2022.9967286","pdf_url":null,"source":{"id":"https://openalex.org/S4363604906","display_name":"2022 IEEE Sensors","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Sensors","raw_type":"proceedings-article"},"type":"article","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/A5051876178","display_name":"Haicui Li","orcid":"https://orcid.org/0000-0003-3424-0339"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Haicui Li","raw_affiliation_strings":["The Graduate School of Computer Science and Engineering, The University of Aizu,Aizu-wakamatsu,Japan","The Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-wakamatsu, Japan"],"affiliations":[{"raw_affiliation_string":"The Graduate School of Computer Science and Engineering, The University of Aizu,Aizu-wakamatsu,Japan","institution_ids":["https://openalex.org/I141591182"]},{"raw_affiliation_string":"The Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-wakamatsu, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100760612","display_name":"Lei Jing","orcid":"https://orcid.org/0000-0002-1181-2536"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Lei Jing","raw_affiliation_strings":["The School of Computer Science and Engineering, The University of Aizu,Aizu-wakamatsu,Japan","The School of Computer Science and Engineering, The University of Aizu, Aizu-wakamatsu, Japan"],"affiliations":[{"raw_affiliation_string":"The School of Computer Science and Engineering, The University of Aizu,Aizu-wakamatsu,Japan","institution_ids":["https://openalex.org/I141591182"]},{"raw_affiliation_string":"The School of Computer Science and Engineering, The University of Aizu, Aizu-wakamatsu, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051876178"],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.6413,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64061557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"99","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9882000088691711,"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"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9882000088691711,"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"}},{"id":"https://openalex.org/T10257","display_name":"Oral microbiology and periodontitis research","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/3506","display_name":"Periodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10368","display_name":"Dental Health and Care Utilization","score":0.9085999727249146,"subfield":{"id":"https://openalex.org/subfields/3506","display_name":"Periodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7351855039596558},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6497585773468018},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4895130395889282},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.4411502778530121},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4032733142375946},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.341705858707428},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.327862024307251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31257712841033936},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2694029211997986}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7351855039596558},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6497585773468018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4895130395889282},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.4411502778530121},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4032733142375946},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.341705858707428},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.327862024307251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31257712841033936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2694029211997986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sensors52175.2022.9967286","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sensors52175.2022.9967286","pdf_url":null,"source":{"id":"https://openalex.org/S4363604906","display_name":"2022 IEEE Sensors","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Sensors","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2057849233","https://openalex.org/W2969598354","https://openalex.org/W3010457767","https://openalex.org/W3213060239"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W4387072315","https://openalex.org/W2180861079","https://openalex.org/W2728376045","https://openalex.org/W2350552870","https://openalex.org/W3005025283","https://openalex.org/W2659933339","https://openalex.org/W2742825757"],"abstract_inverted_index":{"In":[0,23,76],"this":[1,24],"research,":[2],"an":[3,44],"indirect":[4],"method":[5],"based":[6],"on":[7,43,87],"five":[8,33],"force":[9,34,52],"sensors":[10,53],"and":[11,58,90,109,127],"a":[12],"Random":[13],"Forest":[14],"Regression(RFR)":[15],"algorithm":[16],"was":[17,66],"proposed":[18],"to":[19,38,82],"detect":[20],"brushing":[21,41],"force.":[22],"system,":[25],"the":[26,40,51,59,63,69,73,85,97,115,119,133],"toothbrush,":[27],"whose":[28],"handle":[29],"is":[30,36,103,107,112,122,125,130],"fixed":[31],"with":[32],"sensors,":[35],"used":[37,55,67],"simulate":[39],"action":[42],"electronic":[45,64],"balance.":[46],"The":[47,92],"data":[48,60],"obtained":[49],"by":[50],"were":[54,80],"as":[56,68],"features":[57],"collected":[61],"from":[62],"balance":[65],"label":[70],"for":[71,114,132],"training":[72,117,135],"RFR":[74],"model.":[75],"addition,":[77],"5":[78],"subjects":[79],"invited":[81],"participate":[83],"in":[84],"experiment":[86],"both":[88],"user-dependent(UD)":[89],"user-independent(UI).":[91],"experimental":[93],"results":[94],"show":[95],"that":[96],"average":[98,120],"Root":[99],"Mean":[100],"Squared":[101],"Error(RMSE)":[102],"22.08g,":[104],"Standard":[105],"Deviatio(SD)":[106],"5.94g":[108],"Relative":[110,128],"Error":[111,129],"7.36%":[113],"UD":[116],"method;":[118],"RMSE":[121],"37.06g,":[123],"SD":[124],"5.90g":[126],"12.35%":[131],"UI":[134],"method.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}