{"id":"https://openalex.org/W4387421320","doi":"https://doi.org/10.1145/3594739.3610746","title":"Enhanced SHL Recognition Using Machine Learning and Deep Learning Models with Multi-source Data","display_name":"Enhanced SHL Recognition Using Machine Learning and Deep Learning Models with Multi-source Data","publication_year":2023,"publication_date":"2023-10-07","ids":{"openalex":"https://openalex.org/W4387421320","doi":"https://doi.org/10.1145/3594739.3610746"},"language":"en","primary_location":{"id":"doi:10.1145/3594739.3610746","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","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/A5081130877","display_name":"Mengyuan Li","orcid":"https://orcid.org/0000-0001-5519-7689"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengyuan Li","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103184515","display_name":"Jun Zhu","orcid":"https://orcid.org/0009-0007-6049-3280"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Zhu","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101414639","display_name":"Yuanyuan Zhang","orcid":"https://orcid.org/0009-0000-2320-0169"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Zhang","raw_affiliation_strings":["Baixingkefu Network Technology Co., Ltd., China"],"affiliations":[{"raw_affiliation_string":"Baixingkefu Network Technology Co., Ltd., China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101601790","display_name":"Xiaoling Lu","orcid":"https://orcid.org/0000-0002-1854-2532"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoling Lu","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics, Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics, Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081130877"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.4913,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.65704402,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"505","last_page":"510"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9973000288009644,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7863242626190186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7512561082839966},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6871881484985352},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.685758113861084},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6033488512039185},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6008904576301575},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5050051808357239},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.45551884174346924},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41514265537261963},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12558099627494812},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09879845380783081},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.06888735294342041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7863242626190186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7512561082839966},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6871881484985352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.685758113861084},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6033488512039185},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6008904576301575},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5050051808357239},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.45551884174346924},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41514265537261963},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12558099627494812},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09879845380783081},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.06888735294342041},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594739.3610746","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1321730271","display_name":null,"funder_award_id":"72171229","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2883766876","https://openalex.org/W2907123474","https://openalex.org/W3084453745","https://openalex.org/W3084469922","https://openalex.org/W3084911924","https://openalex.org/W3085994804","https://openalex.org/W3200144996","https://openalex.org/W3201972334","https://openalex.org/W3202730452","https://openalex.org/W3202752967","https://openalex.org/W3203725677","https://openalex.org/W3203798346","https://openalex.org/W4387421349"],"related_works":["https://openalex.org/W2383807498","https://openalex.org/W1978572805","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2149980199","https://openalex.org/W4389518428","https://openalex.org/W3094550016","https://openalex.org/W2982650128"],"abstract_inverted_index":{"The":[0],"Sussex-Huawei":[1],"Locomotion-Transportation":[2],"(SHL)":[3],"recognition":[4],"challenge":[5],"collects":[6],"sensor":[7],"data":[8,35],"for":[9],"activity":[10],"recognition,":[11],"garnering":[12],"significant":[13],"interest":[14],"among":[15],"researchers.":[16],"Our":[17],"team,":[18],"named":[19],"\"Juliet\",":[20],"extracts":[21],"features":[22,85,88],"from":[23],"multi-source":[24],"data.":[25],"It":[26],"is":[27],"worth":[28],"noting":[29],"that":[30,50,69,81],"we":[31,62,76],"incorporate":[32],"OpenStreetMap":[33],"(OSM)":[34],"as":[36,89],"supplementary":[37],"information,":[38],"which":[39],"significantly":[40],"improves":[41],"the":[42,93,101],"prediction":[43],"performance.":[44],"We":[45],"employ":[46],"an":[47],"ensemble":[48,94],"model":[49,95],"combines":[51],"both":[52,83],"machine":[53,60],"learning":[54,57],"and":[55,66,86],"deep":[56,74],"techniques.":[58],"For":[59,73],"learning,":[61,75],"utilize":[63],"XGBoost,":[64],"LightGBM,":[65],"CatBoost":[67],"models":[68],"take":[70],"hand-crafted":[71,87],"features.":[72],"adopt":[77],"a":[78],"CNN-RNN-Transformer":[79],"framework":[80],"accepts":[82],"raw":[84],"input.":[90],"By":[91],"combining":[92],"with":[96],"post-smoothing,":[97],"our":[98],"approach":[99],"enhances":[100],"accuracy":[102],"of":[103],"SHL":[104],"recognition.":[105]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"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"}
