{"id":"https://openalex.org/W4312647787","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892502","title":"Deep Randomized Feed-forward Networks Based Prediction of Human Joint Angles Using Wearable Inertial Measurement Unit: Performance Comparison","display_name":"Deep Randomized Feed-forward Networks Based Prediction of Human Joint Angles Using Wearable Inertial Measurement Unit: Performance Comparison","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312647787","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892502"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892502","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5011100373","display_name":"Sibo Yang","orcid":"https://orcid.org/0000-0003-3024-0630"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Sibo Yang","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042919549","display_name":"Ruobin Gao","orcid":"https://orcid.org/0000-0003-0781-1482"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ruobin Gao","raw_affiliation_strings":["School of Civil and Environmental Engineering, Nanyang Technological University,Singapore","School of Civil and Environmental Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Civil and Environmental Engineering, Nanyang Technological University,Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"School of Civil and Environmental Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696013","display_name":"Lei Li","orcid":"https://orcid.org/0000-0001-9887-0175"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Rehabilitation Research Institute of Singapore, Nanyang Technological University"],"affiliations":[{"raw_affiliation_string":"Rehabilitation Research Institute of Singapore, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002794481","display_name":"Wei Tech Ang","orcid":"https://orcid.org/0000-0002-5778-7719"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wei Tech Ang","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University","Rehabilitation Research Institute of Singapore, Nanyang Technological University"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Rehabilitation Research Institute of Singapore, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011100373"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.9619,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74140012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"22","issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9997000098228455,"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/T11023","display_name":"Prosthetics and Rehabilitation Robotics","score":0.9976999759674072,"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/T10510","display_name":"Stroke Rehabilitation and Recovery","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2742","display_name":"Rehabilitation"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8188349008560181},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.7005898356437683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6133326888084412},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5709771513938904},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5661501884460449},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.48942771553993225},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4526398777961731},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.44670259952545166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3971816897392273},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3220706582069397},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07934895157814026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8188349008560181},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.7005898356437683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6133326888084412},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5709771513938904},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5661501884460449},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.48942771553993225},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4526398777961731},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.44670259952545166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3971816897392273},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3220706582069397},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07934895157814026},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892502","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":28,"referenced_works":["https://openalex.org/W1542947501","https://openalex.org/W1565746575","https://openalex.org/W1967159897","https://openalex.org/W1976891673","https://openalex.org/W1994799067","https://openalex.org/W1996640396","https://openalex.org/W2002204150","https://openalex.org/W2012638612","https://openalex.org/W2016210396","https://openalex.org/W2043286593","https://openalex.org/W2054326469","https://openalex.org/W2105352020","https://openalex.org/W2121971770","https://openalex.org/W2122040390","https://openalex.org/W2126663592","https://openalex.org/W2229668941","https://openalex.org/W2286961399","https://openalex.org/W2293590878","https://openalex.org/W2301541953","https://openalex.org/W2474105065","https://openalex.org/W2736191430","https://openalex.org/W2968760063","https://openalex.org/W2974439424","https://openalex.org/W3118598657","https://openalex.org/W3172561953","https://openalex.org/W3199453627","https://openalex.org/W3200093122","https://openalex.org/W4229066889"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W1584764049","https://openalex.org/W2743832667","https://openalex.org/W4386889652"],"abstract_inverted_index":{"Active":[0],"upper":[1,35,123,136],"limb":[2,12,36,124],"assistive":[3,230],"robots":[4],"can":[5,72],"improve":[6],"patients'":[7],"quality":[8],"of":[9,18,33,121,193,201,219,229],"life":[10],"with":[11],"impairment":[13],"and":[14,25,47,81,93,135,160,164,171],"facilitate":[15],"the":[16,34,41,45,68,100,122,126,131,146,183,186,190,199,213,216,226],"rehabilitation":[17],"patients":[19],"in":[20,56,125],"need.":[21],"To":[22],"provide":[23,74],"intuitive":[24],"simultaneous":[26],"active":[27],"assistance,":[28],"continuous":[29,101],"joint":[30,58,102,119,141,151],"angle":[31,103],"prediction":[32,104,191],"is":[37,52,196],"crucial":[38],"for":[39,99,207],"promoting":[40],"interactive":[42],"control":[43,228],"between":[44],"robot":[46],"subjects.":[48],"The":[49,114],"model-free":[50],"approach":[51,71],"gradually":[53],"becoming":[54],"mainstream":[55],"predicting":[57],"angles,":[59],"especially":[60],"those":[61],"based":[62],"on":[63],"deep":[64,158,161,169,172],"neural":[65],"networks.":[66],"However,":[67],"current":[69],"applied":[70],"not":[73],"competitive":[75],"predictive":[76],"performance,":[77],"fast":[78,217],"computation":[79,218],"speed,":[80],"learning":[82,95],"efficiency.":[83],"This":[84],"paper":[85],"implements":[86],"random":[87],"vector":[88],"functional":[89],"link":[90],"networks":[91,97,155,166,221],"(RVFL)":[92],"extreme":[94],"machine":[96],"(ELM)":[98],"using":[105],"only":[106],"data":[107],"from":[108,130],"wearable":[109],"inertial":[110],"measurement":[111],"units":[112],"(IMU).":[113],"input":[115],"features":[116],"are":[117,143,205],"five":[118],"angles":[120,142],"time":[127],"domain,":[128],"derived":[129],"forearm":[132],"attached":[133,138],"IMU":[134],"arm":[137],"IMU.":[139],"Five":[140],"fed":[144],"into":[145],"models":[147],"to":[148,225],"predict":[149],"every":[150],"angle.":[152],"Multiple":[153],"RVFL":[154,194,220],"(Shallow":[156,167],"RVFL,":[157,159],"ensemble":[162,173],"RVFL)":[163],"ELM":[165,202],"ELM,":[168,170],"ELM)":[174],"were":[175],"evaluated":[176],"over":[177],"a":[178],"comprehensive":[179],"experimental":[180],"framework.":[181],"On":[182,212],"one":[184],"hand,":[185,215],"results":[187],"show":[188],"that":[189],"performance":[192],"approaches":[195],"better":[197],"than":[198],"variant":[200],"networks,":[203],"which":[204],"precise":[206],"practical":[208],"robot-assisted":[209],"application":[210],"scenarios.":[211],"other":[214],"offers":[222],"significant":[223],"practicability":[224],"real-time":[227],"robots.":[231]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}