{"id":"https://openalex.org/W4405522497","doi":"https://doi.org/10.1109/iscit63075.2024.10793590","title":"MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors","display_name":"MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors","publication_year":2024,"publication_date":"2024-09-23","ids":{"openalex":"https://openalex.org/W4405522497","doi":"https://doi.org/10.1109/iscit63075.2024.10793590"},"language":"en","primary_location":{"id":"doi:10.1109/iscit63075.2024.10793590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit63075.2024.10793590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 23rd International Symposium on Communications and Information Technologies (ISCIT)","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/A5068798343","display_name":"Sakorn Mekruksavanich","orcid":"https://orcid.org/0000-0002-3735-4262"},"institutions":[{"id":"https://openalex.org/I4210090662","display_name":"University of Phayao","ror":"https://ror.org/00a5mh069","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210090662"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Sakorn Mekruksavanich","raw_affiliation_strings":["School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand","institution_ids":["https://openalex.org/I4210090662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007999637","display_name":"Datchakorn Tancharoen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Datchakorn Tancharoen","raw_affiliation_strings":["Panyapiwat Institute of Management,Faculty of Engineering and Technology,Nonthaburi,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Panyapiwat Institute of Management,Faculty of Engineering and Technology,Nonthaburi,Thailand","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085644461","display_name":"Anuchit Jitpattanakul","orcid":"https://orcid.org/0000-0002-5249-2786"},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Anuchit Jitpattanakul","raw_affiliation_strings":["Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand","institution_ids":["https://openalex.org/I82828225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8749,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76150979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"218","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9383999705314636,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9383999705314636,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6498477458953857},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6389978528022766},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6306171417236328},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6134450435638428},{"id":"https://openalex.org/keywords/inertial-frame-of-reference","display_name":"Inertial frame of reference","score":0.5041781663894653},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.4224564731121063},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36024636030197144},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07916122674942017}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6498477458953857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6389978528022766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6306171417236328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6134450435638428},{"id":"https://openalex.org/C173386949","wikidata":"https://www.wikidata.org/wiki/Q192735","display_name":"Inertial frame of reference","level":2,"score":0.5041781663894653},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.4224564731121063},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36024636030197144},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07916122674942017},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscit63075.2024.10793590","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit63075.2024.10793590","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 23rd International Symposium on Communications and Information Technologies (ISCIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2007678436","https://openalex.org/W2808414691","https://openalex.org/W2913033761","https://openalex.org/W2936695458","https://openalex.org/W2945445771","https://openalex.org/W2979668226","https://openalex.org/W3008284596","https://openalex.org/W3035844538","https://openalex.org/W3182319652","https://openalex.org/W3194178254","https://openalex.org/W3203762492","https://openalex.org/W3207890561","https://openalex.org/W4210718128","https://openalex.org/W4283385629","https://openalex.org/W4293660950","https://openalex.org/W4296400853","https://openalex.org/W4308078775","https://openalex.org/W4313585941","https://openalex.org/W4317727032","https://openalex.org/W4383501641","https://openalex.org/W4385498088","https://openalex.org/W4386961391","https://openalex.org/W4387503284","https://openalex.org/W4390045449","https://openalex.org/W4391168135","https://openalex.org/W4391647110"],"related_works":["https://openalex.org/W2091018038","https://openalex.org/W2225378543","https://openalex.org/W9839718","https://openalex.org/W3110613631","https://openalex.org/W4287122200","https://openalex.org/W2742744817","https://openalex.org/W2040913503","https://openalex.org/W3166845860","https://openalex.org/W2898126303","https://openalex.org/W2382856674"],"abstract_inverted_index":{"Recognition":[0],"of":[1,84,101,116,137],"human":[2],"activities":[3,102],"using":[4,96],"wearable":[5,105],"sensors":[6,23],"is":[7,94],"essential":[8],"for":[9,70],"applications":[10],"related":[11],"to":[12,48,79],"health":[13],"and":[14,45,51,68,130],"well-being.":[15],"However,":[16],"the":[17,77,85,135],"complex":[18],"data":[19],"captured":[20],"by":[21,123],"inertial":[22,106],"poses":[24],"challenges":[25],"in":[26],"accurately":[27],"identifying":[28],"actions.":[29],"This":[30],"research":[31],"introduces":[32],"MotionNeXt,":[33],"a":[34,64,97],"residual":[35,44],"deep":[36,120],"neural":[37],"network":[38],"incorporating":[39],"aggregated":[40],"transformation.":[41],"MotionNeXt":[42,93,133],"utilizes":[43],"multi-kernel":[46],"blocks":[47],"extract":[49],"spatial":[50],"temporal":[52],"characteristics":[53],"from":[54,104],"raw":[55],"IMU":[56],"data.":[57],"Subsequently,":[58],"it":[59],"employs":[60],"global":[61],"average":[62],"pooling,":[63],"fully":[65],"connected":[66],"layer,":[67],"softmax":[69],"classification":[71],"purposes.":[72],"An":[73],"attention":[74],"layer":[75],"enables":[76],"model":[78],"focus":[80],"on":[81],"specific":[82],"segments":[83],"input":[86],"sequence":[87],"while":[88],"categorizing":[89],"each":[90],"time":[91],"step.":[92],"evaluated":[95],"publicly":[98],"available":[99],"dataset":[100],"collected":[103],"measurement":[107],"devices.":[108],"It":[109],"achieves":[110],"state-of-the-art":[111],"accuracy,":[112],"boasting":[113],"an":[114],"F1-score":[115],"99.37%,":[117],"surpassing":[118],"previous":[119],"learning":[121],"methods":[122],"2-":[124],"5%.":[125],"By":[126],"integrating":[127],"accelerometer,":[128],"gyroscope,":[129],"magnetometer":[131],"modalities,":[132],"mitigates":[134],"limitations":[136],"individual":[138],"inputs,":[139],"significantly":[140],"improving":[141],"accuracy":[142],"across":[143],"various":[144],"models.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
