{"id":"https://openalex.org/W6888506968","doi":"https://doi.org/10.21227/x9s8-6558","title":"Waveform-Guide Transformation of IMU Measurements for Pedestrian Dead Reckoning","display_name":"Waveform-Guide Transformation of IMU Measurements for Pedestrian Dead Reckoning","publication_year":2022,"publication_date":"2022-04-13","ids":{"openalex":"https://openalex.org/W6888506968","doi":"https://doi.org/10.21227/x9s8-6558"},"language":"en","primary_location":{"id":"doi:10.21227/x9s8-6558","is_oa":true,"landing_page_url":"https://doi.org/10.21227/x9s8-6558","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/x9s8-6558","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Han, Kyuwon","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Han, Kyuwon","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yu, Seung Min","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161173","display_name":"Korea Railroad Research Institute","ror":"https://ror.org/04gzcxt97","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4210161173","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yu, Seung Min","raw_affiliation_strings":["Korea Railroad Research Institute"],"affiliations":[{"raw_affiliation_string":"Korea Railroad Research Institute","institution_ids":["https://openalex.org/I4210161173"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ko, Seung-Woo","orcid":null},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ko, Seung-Woo","raw_affiliation_strings":["Inha University"],"affiliations":[{"raw_affiliation_string":"Inha University","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kim, Seong-Lyun","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kim, Seong-Lyun","raw_affiliation_strings":["Yonsei University"],"affiliations":[{"raw_affiliation_string":"Yonsei University","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.7504000067710876},{"id":"https://openalex.org/keywords/dead-reckoning","display_name":"Dead reckoning","score":0.6985999941825867},{"id":"https://openalex.org/keywords/swing","display_name":"Swing","score":0.632099986076355},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5558000206947327},{"id":"https://openalex.org/keywords/step-detection","display_name":"Step detection","score":0.45980000495910645},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4018999934196472},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3801000118255615},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3474999964237213}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.7504000067710876},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7347000241279602},{"id":"https://openalex.org/C106165642","wikidata":"https://www.wikidata.org/wiki/Q152255","display_name":"Dead reckoning","level":3,"score":0.6985999941825867},{"id":"https://openalex.org/C65655974","wikidata":"https://www.wikidata.org/wiki/Q14867674","display_name":"Swing","level":2,"score":0.632099986076355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6144000291824341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.590399980545044},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C293773","wikidata":"https://www.wikidata.org/wiki/Q7608015","display_name":"Step detection","level":3,"score":0.45980000495910645},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4018999934196472},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3702999949455261},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3474999964237213},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C151233233","wikidata":"https://www.wikidata.org/wiki/Q47574","display_name":"Units of measurement","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C2777551076","wikidata":"https://www.wikidata.org/wiki/Q842332","display_name":"Front (military)","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C2780226923","wikidata":"https://www.wikidata.org/wiki/Q929848","display_name":"Movement (music)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C100368936","wikidata":"https://www.wikidata.org/wiki/Q1411725","display_name":"Crawling","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.25119999051094055},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/x9s8-6558","is_oa":true,"landing_page_url":"https://doi.org/10.21227/x9s8-6558","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/x9s8-6558","is_oa":true,"landing_page_url":"https://doi.org/10.21227/x9s8-6558","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4357948899269104,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,35,78],"collect":[1,66,97],"IMU":[2],"measurements":[3],"under":[4],"three":[5],"different":[6],"patterns:":[7],"Fixing":[8],"a":[9,18,29],"smartphone":[10,19,30],"in":[11,23,31,72],"front":[12],"of":[13,43,52,59,69],"his":[14,24,32],"chest":[15,44],"(chest),":[16],"swing":[17,53],"while":[20],"holding":[21],"it":[22],"hand":[25],"(swing),":[26],"and":[27,45,54,83,89],"putting":[28],"pocket":[33],"(pocket).":[34],"use":[36],"Google":[37,46],"Pixel":[38,47],"3XL":[39],"for":[40,49,87],"the":[41,50,67],"pattern":[42],"3a":[48],"patterns":[51],"pocket.":[55],"The":[56],"sampling":[57],"frequency":[58],"each":[60],"measurement":[61,68],"is":[62],"fixed":[63],"to":[64,96],"15Hz. We":[65],"111":[70],"paths":[71],"total,":[73],"categorized":[74],"into":[75,81],"4":[76],"types.":[77],"partition":[79],"them":[80],"84":[82],"27":[84],"paths,":[85],"used":[86],"training":[88],"testing,":[90],"respectively.":[91],"It":[92],"takes":[93],"10":[94],"hours":[95],"all":[98],"datasets.":[99]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}