{"id":"https://openalex.org/W3094017527","doi":"https://doi.org/10.1109/icccnt49239.2020.9225419","title":"Effect of Reduced Dimensionality on Deep learning for Human Activity Recognition","display_name":"Effect of Reduced Dimensionality on Deep learning for Human Activity Recognition","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3094017527","doi":"https://doi.org/10.1109/icccnt49239.2020.9225419","mag":"3094017527"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt49239.2020.9225419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt49239.2020.9225419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","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/A5051241286","display_name":"Girja Kumari","orcid":null},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Girja Kumari","raw_affiliation_strings":["Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004564964","display_name":"Jayeeta Chakraborty","orcid":"https://orcid.org/0000-0003-3918-1649"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jayeeta Chakraborty","raw_affiliation_strings":["Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082301428","display_name":"Anup Nandy","orcid":"https://orcid.org/0000-0003-4865-5420"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anup Nandy","raw_affiliation_strings":["Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence and Bio-motion Research lab, National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051241286"],"corresponding_institution_ids":["https://openalex.org/I16292982"],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62443705,"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":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9879999756813049,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9836000204086304,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7701961994171143},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.73397296667099},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6671355962753296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6462641954421997},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6304616332054138},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5982764363288879},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5731964707374573},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5473381280899048},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5383449792861938},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5114527940750122},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4842964708805084},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4740431308746338},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4410703480243683},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4267445206642151},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4260464012622833},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.419107586145401},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.4121192991733551},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4108525514602661},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40007781982421875},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09991982579231262}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7701961994171143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73397296667099},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6671355962753296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6462641954421997},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6304616332054138},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5982764363288879},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5731964707374573},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5473381280899048},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5383449792861938},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5114527940750122},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4842964708805084},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4740431308746338},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4410703480243683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4267445206642151},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4260464012622833},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.419107586145401},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.4121192991733551},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4108525514602661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40007781982421875},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09991982579231262},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt49239.2020.9225419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt49239.2020.9225419","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","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":44,"referenced_works":["https://openalex.org/W76865604","https://openalex.org/W82044129","https://openalex.org/W119403003","https://openalex.org/W1622741890","https://openalex.org/W1923002919","https://openalex.org/W1924178503","https://openalex.org/W1984489625","https://openalex.org/W1986870606","https://openalex.org/W2011329709","https://openalex.org/W2105046342","https://openalex.org/W2121269968","https://openalex.org/W2128121198","https://openalex.org/W2131368080","https://openalex.org/W2135018291","https://openalex.org/W2136435976","https://openalex.org/W2148857358","https://openalex.org/W2149216032","https://openalex.org/W2159613676","https://openalex.org/W2171145593","https://openalex.org/W2195342085","https://openalex.org/W2219995598","https://openalex.org/W2270470215","https://openalex.org/W2491430782","https://openalex.org/W2494054254","https://openalex.org/W2519804223","https://openalex.org/W2540822603","https://openalex.org/W2550797816","https://openalex.org/W2562935526","https://openalex.org/W2563686712","https://openalex.org/W2620371446","https://openalex.org/W2621205740","https://openalex.org/W2621464581","https://openalex.org/W2736191430","https://openalex.org/W2790849663","https://openalex.org/W2887886819","https://openalex.org/W2889505988","https://openalex.org/W2963855167","https://openalex.org/W3001191590","https://openalex.org/W4210731039","https://openalex.org/W6682035786","https://openalex.org/W6722953049","https://openalex.org/W6728817311","https://openalex.org/W6749302402","https://openalex.org/W6990696534"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W4320026695","https://openalex.org/W4311106074","https://openalex.org/W2126100045","https://openalex.org/W2166303055","https://openalex.org/W2160451891","https://openalex.org/W3011717444","https://openalex.org/W3004034588","https://openalex.org/W4244536162","https://openalex.org/W2091615597"],"abstract_inverted_index":{"Inertial":[0],"sensor":[1,183],"is":[2,80,91,100,176],"used":[3,68,177],"to":[4,12,44,66,114,134,216],"measure":[5,251],"angular":[6],"velocity,":[7],"acceleration":[8],"etc":[9],"when":[10],"attached":[11],"a":[13,49,70,85,159],"human":[14,46,264],"body":[15],"part":[16],"while":[17],"performing":[18],"daily":[19],"or":[20],"sports":[21,170],"activities.":[22,47,116],"Deep":[23],"learning":[24],"classifier":[25],"models":[26],"such":[27,144],"as":[28,123,145],"Long":[29],"Short-term":[30],"Memory":[31],"(LSTM)":[32],"networks":[33],"are":[34,132,252,260],"capable":[35],"of":[36,58,87,105,110,191,227],"extracting":[37],"high":[38],"dimensional":[39],"features":[40,131,214,228,262],"from":[41,93],"signal":[42,79,113],"sequences":[43,104],"represent":[45,115],"However,":[48],"trade-off":[50],"between":[51],"the":[52,56,77,103,111,136,156],"computational":[53],"complexity":[54],"and":[55,84,126,151,194,207,234,239,248,258],"performance":[57],"activity":[59,265],"recognition":[60],"system":[61,157],"should":[62],"be":[63,67],"maintained":[64],"if":[65],"in":[69,197,255],"low":[71],"resource":[72],"device.":[73],"In":[74],"this":[75],"paper,":[76],"original":[78,112],"segmented":[81],"into":[82],"windows":[83],"set":[86],"statistical":[88],"feature":[89,107,118,127,250],"measures":[90,108],"extracted":[92,213],"each":[94],"window.":[95],"A":[96],"stacked":[97,137],"LSTM":[98,138],"network":[99,139],"trained":[101],"with":[102,141,179,224,232],"concatenated":[106],"instead":[109],"Using":[117],"selection":[119],"methods:":[120],"correlation":[121,210],"metric":[122,211],"heat":[124],"map":[125],"importance,":[128],"only":[129],"significant":[130],"selected":[133],"train":[135],"along":[140,178],"conventional":[142,230],"classifiers":[143,206,231],"support":[146],"vector":[147],"machine,":[148],"random":[149],"forest":[150],"k-nearest":[152],"neighbor.":[153],"To":[154],"evaluate":[155],"performance,":[158,218],"new":[160,188],"dataset":[161,189,241],"collection,":[162],"named":[163],"Human":[164],"Physical":[165],"Activities":[166],"(HPA)":[167],"dataset,":[168,185],"for":[169,204,237,263],"activities":[171],"using":[172],"four":[173],"inertial":[174,182],"sensors":[175],"an":[180],"existing":[181],"based":[184],"HuGaBD.":[186],"The":[187,200],"consists":[190],"nine":[192],"static":[193],"dynamic":[195],"movements":[196],"outdoor":[198],"setting.":[199],"result":[201,215],"shows":[202,245],"that":[203,246],"all":[205],"both":[208],"datasets,":[209],"heatmap":[212],"better":[217],"except":[219],"precision":[220],"values.":[221],"LSTM,":[222],"even":[223],"limited":[225],"number":[226],"outperformed,":[229],"91.1%":[233],"93.4%":[235],"accuracy":[236,257],"HuGaBD":[238],"HPA":[240],"respectively.":[242],"Experimental":[243],"analysis":[244],"entropy":[247],"median":[249],"highly":[253],"contributing":[254],"classification":[256],"theses":[259],"essential":[261],"recognition.":[266]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-12-01T00:03:43.161839","created_date":"2025-10-10T00:00:00"}