{"id":"https://openalex.org/W7009508621","doi":null,"title":"Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition","display_name":"Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W7009508621"},"language":"en","primary_location":{"id":"pmh:oai:DiVA.org:kth-325723","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325723","pdf_url":"https://kth.diva-portal.org/smash/get/diva2:1750380/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://kth.diva-portal.org/smash/get/diva2:1750380/FULLTEXT01","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bj\u00f6rnsson, Helgi Hrafn","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Bj\u00f6rnsson, Helgi Hrafn","raw_affiliation_strings":["KTH Royal Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kaldal, J\u00f3n","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Kaldal, J\u00f3n","raw_affiliation_strings":["KTH Royal Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.5913000106811523,"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":0.5913000106811523,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.21690000593662262,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.026399999856948853,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/human-activity-recognition","display_name":"human activity recognition","score":1.0},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.7269999980926514},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.847000002861023},{"id":"https://openalex.org/keywords/gru","display_name":"GRU","score":0.843999981880188},{"id":"https://openalex.org/keywords/resource-constrained-devices","display_name":"resource-constrained devices","score":0.7059999704360962},{"id":"https://openalex.org/keywords/embedded-devices","display_name":"embedded devices","score":0.7559999823570251},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.5690000057220459},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"energy consumption","score":0.7609999775886536},{"id":"https://openalex.org/keywords/memory-usage","display_name":"memory usage","score":0.7179999947547913},{"id":"https://openalex.org/keywords/tensorflow-lite-micro","display_name":"TensorFlow Lite Micro","score":0.734000027179718},{"id":"https://openalex.org/keywords/time-series-classification","display_name":"time series classification","score":0.6690000295639038}],"concepts":[{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.832099974155426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7990000247955322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6741999983787537},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6556000113487244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5462999939918518},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5169000029563904},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4878000020980835},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.36820000410079956},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:DiVA.org:kth-325723","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325723","pdf_url":"https://kth.diva-portal.org/smash/get/diva2:1750380/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"}],"best_oa_location":{"id":"pmh:oai:DiVA.org:kth-325723","doi":null,"is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325723","pdf_url":"https://kth.diva-portal.org/smash/get/diva2:1750380/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I86987016","host_organization_name":"KTH Royal Institute of Technology","host_organization_lineage":["https://openalex.org/I86987016"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bachelorThesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7009508621.pdf","grobid_xml":"https://content.openalex.org/works/W7009508621.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W2017634428","https://openalex.org/W2040870580","https://openalex.org/W2054780155","https://openalex.org/W2103496339","https://openalex.org/W2119144962","https://openalex.org/W2159613676","https://openalex.org/W2162739315","https://openalex.org/W2337546824","https://openalex.org/W2736191430","https://openalex.org/W2797583228","https://openalex.org/W2936503027","https://openalex.org/W2972660824","https://openalex.org/W2975417752","https://openalex.org/W2981832419","https://openalex.org/W3017181332","https://openalex.org/W3092134948","https://openalex.org/W3123271772","https://openalex.org/W3130853400","https://openalex.org/W3139646219","https://openalex.org/W3157803868","https://openalex.org/W4210529021","https://openalex.org/W4214843096"],"related_works":["https://openalex.org/W4287964677","https://openalex.org/W4388966870","https://openalex.org/W4392238451","https://openalex.org/W4416960475","https://openalex.org/W4409691894","https://openalex.org/W4404432711","https://openalex.org/W4292722085","https://openalex.org/W3209735728","https://openalex.org/W4312889818","https://openalex.org/W6981001476"],"abstract_inverted_index":{"Human":[0],"activity":[1,100,121,202],"data":[2],"is":[3,45,57,151],"typically":[4],"represented":[5],"as":[6,181],"time":[7],"series":[8],"data,":[9],"and":[10,26,135,146,164,193,211],"RNNs,":[11,132],"often":[12,29],"with":[13,207],"LSTM":[14,182],"cells,":[15],"are":[16,28],"commonly":[17],"used":[18,97],"for":[19,32,200],"recognition":[20,203],"in":[21,59,138,184],"this":[22,80,171],"field.":[23],"However,":[24],"RNNs":[25],"LSTM-RNNs":[27],"too":[30],"resource-intensive":[31],"real-time":[33],"applications":[34],"on":[35,72,98,122,158,204],"resource":[36],"constrained":[37],"devices,":[38],"making":[39,195],"them":[40],"unsuitable.":[41],"This":[42,102,149],"thesis":[43,103,172],"project":[44,81],"carried":[46],"out":[47],"at":[48],"Wrlds":[49],"AB,":[50],"Stockholm.":[51],"At":[52],"Wrlds,":[53],"all":[54],"machine":[55],"learning":[56,112],"run":[58,68],"the":[60,90,93,105,155,175],"cloud,":[61],"but":[62],"they":[63],"have":[64],"been":[65],"attempting":[66],"to":[67,83,88,95,118,130,153],"their":[69,73,162],"AI":[70],"algorithms":[71],"embedded":[74,205],"devices.":[75,124],"The":[76,125,168],"main":[77],"task":[78],"of":[79,92,107,140,170,186],"was":[82,128],"investigate":[84],"alternative":[85],"network":[86,177],"structures":[87],"minimize":[89],"size":[91],"networks":[94,183],"be":[96],"human":[99,120,201],"data.":[101],"investigates":[104],"use":[106],"Fast":[108],"GRNN,":[109],"a":[110,197],"deep":[111],"algorithm":[113,127,157],"developed":[114],"by":[115],"Microsoft":[116],"researchers,":[117],"classify":[119],"resource-constrained":[123],"FastGRNN":[126],"compared":[129],"state-of-the-art":[131],"LSTM,":[133],"GRU,":[134],"Simple":[136],"RNN":[137],"terms":[139,185],"accuracy,":[141],"classification":[142],"time,":[143],"memory":[144],"usage,":[145],"energy":[147],"consumption.":[148],"research":[150],"limited":[152,208],"implementing":[154],"FastRNN":[156],"Nordic":[159],"SoCs":[160],"using":[161],"SDK":[163],"TensorFlow":[165],"Lite":[166],"Micro.":[167],"result":[169],"shows":[173],"that":[174],"proposed":[176],"has":[178],"similar":[179],"performance":[180],"accuracy":[187],"while":[188],"being":[189],"both":[190],"considerably":[191],"smaller":[192],"faster,":[194],"it":[196],"promising":[198],"solution":[199],"devices":[206],"computational":[209],"resources":[210],"merits":[212],"further":[213],"investigation.":[214]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
