{"id":"https://openalex.org/W2973029833","doi":"https://doi.org/10.1145/3341162.3345594","title":"A dialogue-based annotation for activity recognition","display_name":"A dialogue-based annotation for activity recognition","publication_year":2019,"publication_date":"2019-09-09","ids":{"openalex":"https://openalex.org/W2973029833","doi":"https://doi.org/10.1145/3341162.3345594","mag":"2973029833"},"language":"en","primary_location":{"id":"doi:10.1145/3341162.3345594","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3345594","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 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","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/A5060309625","display_name":"Tittaya Mairittha","orcid":"https://orcid.org/0000-0002-4250-7964"},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tittaya Mairittha","raw_affiliation_strings":["Kyushu Institute of Technology, Fukuoka, JAPAN"],"affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology, Fukuoka, JAPAN","institution_ids":["https://openalex.org/I207014233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003778178","display_name":"Nattaya Mairittha","orcid":"https://orcid.org/0000-0001-9979-2412"},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nattaya Mairittha","raw_affiliation_strings":["Kyushu Institute of Technology, Fukuoka, JAPAN"],"affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology, Fukuoka, JAPAN","institution_ids":["https://openalex.org/I207014233"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080895628","display_name":"Sozo Inoue","orcid":"https://orcid.org/0000-0003-1109-8130"},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sozo Inoue","raw_affiliation_strings":["Kyushu Institute of Technology, Fukuoka, JAPAN"],"affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology, Fukuoka, JAPAN","institution_ids":["https://openalex.org/I207014233"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060309625"],"corresponding_institution_ids":["https://openalex.org/I207014233"],"apc_list":null,"apc_paid":null,"fwci":0.5061,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69396524,"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":"768","last_page":"773"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9997000098228455,"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":0.9997000098228455,"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/T12031","display_name":"Speech and dialogue systems","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9783999919891357,"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/annotation","display_name":"Annotation","score":0.8350769281387329},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7640810012817383},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.646865963935852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49051111936569214},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4901962876319885},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4519686996936798},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.34150511026382446},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3408975601196289},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3267785608768463},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.16544833779335022}],"concepts":[{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.8350769281387329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7640810012817383},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.646865963935852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49051111936569214},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4901962876319885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4519686996936798},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.34150511026382446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3408975601196289},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3267785608768463},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.16544833779335022}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341162.3345594","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341162.3345594","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 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W182831726","https://openalex.org/W1594031697","https://openalex.org/W1748932423","https://openalex.org/W1933349210","https://openalex.org/W1969307352","https://openalex.org/W2021717943","https://openalex.org/W2023302299","https://openalex.org/W2155983176","https://openalex.org/W2162762857","https://openalex.org/W2251758222","https://openalex.org/W2489609634","https://openalex.org/W2899118219","https://openalex.org/W4300311693"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W1533177136","https://openalex.org/W4380994516","https://openalex.org/W2610740816"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,15,57],"method":[4],"to":[5,55],"collect":[6],"training":[7],"labels":[8],"for":[9,86],"human":[10],"activity":[11,42],"recognition":[12,43],"by":[13],"using":[14,23],"dialogue":[16,29],"system.":[17],"To":[18],"show":[19],"the":[20,28,35,45,52,65,80],"feasibility":[21],"of":[22,41,47,60],"dialogue-based":[24],"annotation,":[25],"we":[26,78],"implemented":[27],"system":[30],"and":[31,72,83],"conducted":[32],"experiments":[33],"in":[34],"lab":[36],"setting.":[37],"The":[38],"preliminary":[39],"performance":[40],"attained":[44],"f-measure":[46],"0.76.":[48],"We":[49],"also":[50],"analyze":[51],"collected":[53],"data":[54],"provide":[56],"better":[58],"understanding":[59],"what":[61],"users":[62],"expect":[63],"from":[64],"system,":[66],"how":[67],"they":[68],"interact":[69],"with":[70],"it":[71],"its":[73],"other":[74],"potential":[75],"uses.":[76],"Finally,":[77],"discussed":[79],"results":[81],"obtained":[82],"possible":[84],"directions":[85],"future":[87],"works.":[88]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
