{"id":"https://openalex.org/W3160127621","doi":"https://doi.org/10.1109/kst51265.2021.9415857","title":"Improved Step Detection with Smartphone Handheld Mode Recognition","display_name":"Improved Step Detection with Smartphone Handheld Mode Recognition","publication_year":2021,"publication_date":"2021-01-21","ids":{"openalex":"https://openalex.org/W3160127621","doi":"https://doi.org/10.1109/kst51265.2021.9415857","mag":"3160127621"},"language":"en","primary_location":{"id":"doi:10.1109/kst51265.2021.9415857","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kst51265.2021.9415857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Knowledge and Smart Technology (KST)","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/A5054713168","display_name":"Warnnaphorn Suksuganjana","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Warnnaphorn Suksuganjana","raw_affiliation_strings":["School of ICT, Sirindhorn International Institute of Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"School of ICT, Sirindhorn International Institute of Technology, Thailand","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018258563","display_name":"Seksan Laitrakun","orcid":"https://orcid.org/0000-0001-6451-9977"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seksan Laitrakun","raw_affiliation_strings":["School of ICT, Sirindhorn International Institute of Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"School of ICT, Sirindhorn International Institute of Technology, Thailand","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067424365","display_name":"Krit Athikulwongse","orcid":"https://orcid.org/0000-0002-4092-8634"},"institutions":[{"id":"https://openalex.org/I14316845","display_name":"National Electronics and Computer Technology Center","ror":"https://ror.org/04z82ry91","country_code":"TH","type":"government","lineage":["https://openalex.org/I1332092204","https://openalex.org/I14316845"]},{"id":"https://openalex.org/I1332092204","display_name":"National Science and Technology Development Agency","ror":"https://ror.org/04vy95b61","country_code":"TH","type":"government","lineage":["https://openalex.org/I1332092204"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Krit Athikulwongse","raw_affiliation_strings":["National Electronics and Computer Technology Center, NSTDA, Thailand"],"affiliations":[{"raw_affiliation_string":"National Electronics and Computer Technology Center, NSTDA, Thailand","institution_ids":["https://openalex.org/I14316845","https://openalex.org/I1332092204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065843430","display_name":"Yuko Hara\u2013Azumi","orcid":"https://orcid.org/0000-0001-9486-5272"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuko Hara-Azumi","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080071277","display_name":"Somrudee Deepaisarn","orcid":"https://orcid.org/0000-0001-7647-6345"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Somrudee Deepaisarn","raw_affiliation_strings":["School of ICT, Sirindhorn International Institute of Technology, Thailand"],"affiliations":[{"raw_affiliation_string":"School of ICT, Sirindhorn International Institute of Technology, Thailand","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054713168"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3008,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.54852394,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12740","display_name":"Gait Recognition and Analysis","score":0.9976999759674072,"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/T10860","display_name":"Speech and Audio Processing","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7276253700256348},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.723178505897522},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.7131125330924988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6940487623214722},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5949468612670898},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5364978909492493},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.5251983404159546},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4732307195663452},{"id":"https://openalex.org/keywords/swing","display_name":"Swing","score":0.46831732988357544},{"id":"https://openalex.org/keywords/step-detection","display_name":"Step detection","score":0.4579258859157562},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4242743253707886},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16071480512619019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276253700256348},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.723178505897522},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.7131125330924988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6940487623214722},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5949468612670898},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5364978909492493},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.5251983404159546},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4732307195663452},{"id":"https://openalex.org/C65655974","wikidata":"https://www.wikidata.org/wiki/Q14867674","display_name":"Swing","level":2,"score":0.46831732988357544},{"id":"https://openalex.org/C293773","wikidata":"https://www.wikidata.org/wiki/Q7608015","display_name":"Step detection","level":3,"score":0.4579258859157562},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4242743253707886},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16071480512619019},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/kst51265.2021.9415857","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kst51265.2021.9415857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Knowledge and Smart Technology (KST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322437","display_name":"National Science and Technology Development Agency","ror":"https://ror.org/04vy95b61"},{"id":"https://openalex.org/F4320322704","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960"},{"id":"https://openalex.org/F4320335051","display_name":"Sirindhorn International Institute of Technology, Thammasat University","ror":"https://ror.org/002yp7f20"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2011224697","https://openalex.org/W2025797281","https://openalex.org/W2071077565","https://openalex.org/W2076787343","https://openalex.org/W2100989187","https://openalex.org/W2108482152","https://openalex.org/W2112796928","https://openalex.org/W2138572279","https://openalex.org/W2319208717","https://openalex.org/W2514344688","https://openalex.org/W2531952511","https://openalex.org/W2559956599","https://openalex.org/W2567482009","https://openalex.org/W2586574952","https://openalex.org/W2769414688","https://openalex.org/W2809202603","https://openalex.org/W2889409040","https://openalex.org/W2898418149","https://openalex.org/W2908979191","https://openalex.org/W2910866633","https://openalex.org/W2913286297","https://openalex.org/W2945555875"],"related_works":["https://openalex.org/W2992410632","https://openalex.org/W2768717251","https://openalex.org/W2025756212","https://openalex.org/W2526466046","https://openalex.org/W2572501918","https://openalex.org/W4281973980","https://openalex.org/W2019976143","https://openalex.org/W4387067991","https://openalex.org/W3003072553","https://openalex.org/W3022152738"],"abstract_inverted_index":{"Indoor":[0],"positioning":[1],"is":[2,21,38,71,101,133,195],"becoming":[3],"an":[4,217],"essential":[5],"aspect":[6],"of":[7,36,44,49,177,187,201],"many":[8],"applications.":[9],"Usually,":[10],"the":[11,34,41,107,118,124,146,154,163,168,178,183,188,198,205,209,213,232,237],"inertial":[12],"measurement":[13],"unit":[14],"(IMU)-based":[15],"pedestrian":[16],"dead":[17],"reckoning":[18],"(PDR)":[19],"system":[20],"preferred":[22],"due":[23],"to":[24,32,39,86,135,144,153],"its":[25],"simple":[26],"and":[27,96,137,172,190],"cost-effective":[28],"structure.":[29],"One":[30],"method":[31,65,143],"enhance":[33],"accuracy":[35,218,238],"PDR":[37],"detect":[40,138],"walking":[42],"steps":[43],"pedestrians":[45],"accurately.":[46],"The":[47,98],"error":[48],"existing":[50],"step":[51,63,125,150,234],"detection":[52,64,126,131,151,235],"methods":[53],"grows":[54],"with":[55,103,197,228],"changes":[56],"in":[57,73,167,222],"handheld":[58,69,90,156,225],"mode.":[59,157],"Therefore,":[60],"a":[61,79,128,142,229],"novel":[62],"by":[66,111],"considering":[67],"smartphone":[68,89,155,224],"modes":[70,91],"proposed":[72,233],"this":[74],"study.":[75],"We":[76,140],"first":[77,184],"construct":[78],"convolutional":[80],"neural":[81],"network":[82],"(CNN)":[83],"classification":[84],"model":[85,100],"recognize":[87],"four":[88],"(texting,":[92],"calling,":[93,169],"pant's":[94,170],"pocket,":[95,171],"swing).":[97],"CNN":[99,214],"trained":[102],"features":[104],"extracted":[105],"from":[106],"IMU":[108],"signals,":[109],"characterized":[110],"scalogram":[112],"images":[113],"that":[114,212],"are":[115],"generated":[116],"through":[117],"continuous":[119],"wavelet":[120],"transform":[121],"(CWT).":[122],"In":[123,175],"phase,":[127],"threshold-based":[129],"peak":[130],"approach":[132],"used":[134],"count":[136],"steps.":[139],"propose":[141],"select":[145],"distinct":[147],"signal":[148,166],"for":[149],"corresponding":[152],"To":[158],"be":[159],"specific,":[160],"we":[161,181],"use":[162,182],"z-axis":[164],"acceleration":[165,192],"texting":[173],"modes.":[174,226],"case":[176],"swing":[179],"mode,":[180],"principal":[185],"component":[186],"x":[189],"y-axes":[191],"signal,":[193],"which":[194],"aligned":[196],"horizontal":[199],"direction":[200],"arm":[202],"swing.":[203],"After":[204],"five-fold":[206],"cross":[207],"validation,":[208],"results":[210],"demonstrated":[211],"classifier":[215],"achieved":[216],"higher":[219],"than":[220],"98%":[221],"all":[223],"Compared":[227],"conventional":[230],"method,":[231],"improved":[236],"around":[239],"0.14%-5.40%.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
