{"id":"https://openalex.org/W2899094700","doi":"https://doi.org/10.1145/3267305.3267520","title":"Hybrid and Convolutional Neural Networks for Locomotion Recognition","display_name":"Hybrid and Convolutional Neural Networks for Locomotion Recognition","publication_year":2018,"publication_date":"2018-10-08","ids":{"openalex":"https://openalex.org/W2899094700","doi":"https://doi.org/10.1145/3267305.3267520","mag":"2899094700"},"language":"en","primary_location":{"id":"doi:10.1145/3267305.3267520","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3267305.3267520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers","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/A5002599627","display_name":"Aomar Osmani","orcid":"https://orcid.org/0000-0002-1778-6527"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Aomar Osmani","raw_affiliation_strings":["LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, Villetaneuse"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, Villetaneuse","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I204730241","https://openalex.org/I4210091437"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048133789","display_name":"Massinissa Hamidi","orcid":"https://orcid.org/0000-0002-1211-3526"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I4210091437","display_name":"Sorbonne Paris Cit\u00e9","ror":"https://ror.org/001z21q04","country_code":"FR","type":"other","lineage":["https://openalex.org/I4210091437"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Massinissa Hamidi","raw_affiliation_strings":["LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, Villetaneuse"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIPN-UMR CNRS 7030, PRES Sorbonne Paris Cit\u00e9, Villetaneuse","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I204730241","https://openalex.org/I4210091437"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6845,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.82490675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1531","last_page":"1540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994999766349792,"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":0.9994999766349792,"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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9955999851226807,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9919999837875366,"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/computer-science","display_name":"Computer science","score":0.7588304877281189},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7132202386856079},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6484993696212769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6273084282875061},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.6081709861755371},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5524533987045288},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5093163251876831},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44331610202789307},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.43839892745018005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4122546911239624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7588304877281189},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7132202386856079},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6484993696212769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6273084282875061},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.6081709861755371},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5524533987045288},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5093163251876831},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44331610202789307},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.43839892745018005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4122546911239624},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3267305.3267520","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3267305.3267520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W182951147","https://openalex.org/W2023302299","https://openalex.org/W2064675550","https://openalex.org/W2092126505","https://openalex.org/W2101234009","https://openalex.org/W2131570437","https://openalex.org/W2145550673","https://openalex.org/W2553915786","https://openalex.org/W2559655401","https://openalex.org/W2604272474","https://openalex.org/W2767174885","https://openalex.org/W2883766876","https://openalex.org/W2899151654","https://openalex.org/W2953384591","https://openalex.org/W4256361765","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W2250140200","https://openalex.org/W3121791438"],"abstract_inverted_index":{"This":[0,17,58],"paper":[1],"explores":[2],"the":[3,22,37,42,49,55,62,107,121],"relevance":[4],"of":[5,31,41,54,65,99,116],"an":[6],"approach":[7],"based":[8],"exclusively":[9],"on":[10,102],"deep":[11],"neural":[12],"networks":[13],"for":[14,45,93],"locomotion":[15],"recognition.":[16],"work":[18],"is":[19,39,91,104],"done":[20],"within":[21],"Sussex-Huawei":[23],"Locomotion-Transportation":[24],"(SHL)":[25],"recognition":[26,114],"challenge":[27],"as":[28],"team":[29],"Power":[30],"Things.":[32],"Provided":[33],"data":[34],"used":[35,92],"during":[36],"experiments":[38],"part":[40],"SHL":[43],"dataset":[44,69],"which":[46],"we":[47],"emphasize":[48],"adaptability":[50],"to":[51],"different":[52,79],"applications":[53],"ubiquitous":[56],"computing.":[57],"quality":[59],"emerges":[60],"from":[61],"broad":[63],"spectrum":[64],"modalities":[66],"that":[67],"this":[68],"encompasses,":[70],"they":[71],"are":[72,84],"16":[73],"in":[74],"total.":[75],"More":[76],"than":[77],"500":[78],"convolutional":[80],"and":[81,86],"hybrid":[82],"architectures":[83],"evaluated,":[85],"a":[87,113],"Bayesian":[88],"optimization":[89],"procedure":[90],"hyper-parameters":[94,101],"space":[95],"exploration.":[96],"The":[97],"influence":[98],"these":[100],"performances":[103],"analyzed":[105],"using":[106],"fANOVA":[108],"framework.":[109],"Best":[110],"models":[111],"achieve":[112],"rate":[115],"about":[117],"92%":[118],"measured":[119],"by":[120],"f1":[122],"score.":[123]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}