{"id":"https://openalex.org/W4366966886","doi":"https://doi.org/10.1145/3544793.3560404","title":"Multimodal Surface Sensing based on Hybrid Flexible Triboelectric and Piezoresistive Sensor","display_name":"Multimodal Surface Sensing based on Hybrid Flexible Triboelectric and Piezoresistive Sensor","publication_year":2022,"publication_date":"2022-09-11","ids":{"openalex":"https://openalex.org/W4366966886","doi":"https://doi.org/10.1145/3544793.3560404"},"language":"en","primary_location":{"id":"doi:10.1145/3544793.3560404","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560404","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560404","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560404","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113155997","display_name":"Zenan Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zenan Lin","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China"],"raw_orcid":"https://orcid.org/0000-0002-0597-4512","affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041376214","display_name":"Kai Chong Lei","orcid":"https://orcid.org/0000-0001-5385-3208"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Chong Lei","raw_affiliation_strings":["Shenzhen International Graduate School, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053302545","display_name":"Shilong Mu","orcid":"https://orcid.org/0009-0004-3638-6539"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shilong Mu","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065627737","display_name":"Ziwu Song","orcid":"https://orcid.org/0000-0002-9234-1800"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwu Song","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079838169","display_name":"Yuan Dai","orcid":"https://orcid.org/0000-0002-6705-1342"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Dai","raw_affiliation_strings":["Tencent Robotics X Lab, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Robotics X Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084510549","display_name":"Wenbo Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Ding","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100363166","display_name":"Xiaoping Zhang","orcid":"https://orcid.org/0000-0003-0046-211X"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CA","CN"],"is_corresponding":false,"raw_author_name":"Xiao-Ping Zhang","raw_affiliation_strings":["Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China and Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua-Berkeley Shenzhen Institute, Shenzhen International Graduate School, Tsinghua University, China and Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Canada","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089","https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5113155997"],"corresponding_institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.327,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.52476493,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"421","last_page":"426"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":1.0,"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/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":1.0,"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/T10914","display_name":"Tactile and Sensory Interactions","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9959999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7205691337585449},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6374666094779968},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6278219819068909},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6170631647109985},{"id":"https://openalex.org/keywords/piezoresistive-effect","display_name":"Piezoresistive effect","score":0.6000778079032898},{"id":"https://openalex.org/keywords/tactile-sensor","display_name":"Tactile sensor","score":0.5388803482055664},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5109032988548279},{"id":"https://openalex.org/keywords/triboelectric-effect","display_name":"Triboelectric effect","score":0.44244804978370667},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4233781695365906},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22111424803733826},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.11158579587936401},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.08406004309654236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7205691337585449},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6374666094779968},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6278219819068909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6170631647109985},{"id":"https://openalex.org/C198490522","wikidata":"https://www.wikidata.org/wiki/Q1932915","display_name":"Piezoresistive effect","level":2,"score":0.6000778079032898},{"id":"https://openalex.org/C46722567","wikidata":"https://www.wikidata.org/wiki/Q7674139","display_name":"Tactile sensor","level":3,"score":0.5388803482055664},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5109032988548279},{"id":"https://openalex.org/C80640880","wikidata":"https://www.wikidata.org/wiki/Q876377","display_name":"Triboelectric effect","level":2,"score":0.44244804978370667},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4233781695365906},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22111424803733826},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.11158579587936401},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.08406004309654236},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3544793.3560404","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560404","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560404","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3544793.3560404","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560404","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560404","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1379700071","display_name":null,"funder_award_id":"2020GQG1004","funder_id":"https://openalex.org/F4320322392","funder_display_name":"Tsinghua University"}],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4366966886.pdf","grobid_xml":"https://content.openalex.org/works/W4366966886.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1995213905","https://openalex.org/W2135991480","https://openalex.org/W2715135432","https://openalex.org/W2727640090","https://openalex.org/W2735680319","https://openalex.org/W2770486845","https://openalex.org/W2783323081","https://openalex.org/W2783338612","https://openalex.org/W2791836551","https://openalex.org/W2901069188","https://openalex.org/W2902546013","https://openalex.org/W2911918145","https://openalex.org/W2967141048","https://openalex.org/W2972810968","https://openalex.org/W3015417033","https://openalex.org/W3083891030","https://openalex.org/W3091940685","https://openalex.org/W3104441645","https://openalex.org/W3114215364","https://openalex.org/W3158837684","https://openalex.org/W4200341868","https://openalex.org/W4220794904"],"related_works":["https://openalex.org/W4281550346","https://openalex.org/W2154907448","https://openalex.org/W2187628146","https://openalex.org/W2622859989","https://openalex.org/W2373005596","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W4207016008","https://openalex.org/W4287600368","https://openalex.org/W3193565141"],"abstract_inverted_index":{"Sensing":[0],"the":[1,7,45,78,88,117],"surface":[2,41],"properties":[3],"through":[4],"touch,":[5],"as":[6],"most":[8],"natural":[9],"perceptual":[10],"way":[11],"of":[12,90,142],"humans,":[13],"has":[14,151],"become":[15],"an":[16],"important":[17],"and":[18,25,47,123,140,144,160],"practical":[19],"method":[20,73],"for":[21,39],"human-machine":[22],"interactions":[23],"(HMI)":[24],"robot":[26,60],"manipulations.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31],"design":[32],"a":[33,58,64,96,102],"fingertip":[34],"hybrid":[35],"flexible":[36],"tactile":[37,52],"sensor":[38],"multimodal":[40],"sensing,":[42],"based":[43],"on":[44,57,129],"triboelectric":[46],"piezoresistive":[48],"mechanisms.":[49],"A":[50,69],"real-time":[51],"sensing":[53],"system":[54,150],"is":[55,74,105],"implemented":[56],"3D-printed":[59],"finger":[61],"together":[62],"with":[63,101,125],"wireless":[65],"data":[66,71,91],"acquisition":[67],"board.":[68],"virtual":[70],"generation":[72],"proposed":[75,149],"to":[76],"expand":[77],"model":[79,100,115],"adaptability":[80],"under":[81],"different":[82],"compression":[83],"force":[84],"levels.":[85],"Moreover,":[86],"considering":[87],"characteristics":[89],"generated":[92],"by":[93],"our":[94],"sensors,":[95],"novel":[97],"deep":[98],"learning":[99],"residual":[103,109],"structure":[104],"developed,":[106],"named":[107],"parallel":[108],"convolutional":[110],"neural":[111],"network":[112],"(PR-CNN).":[113],"Our":[114],"outperforms":[116],"state-of-the-art":[118],"models,":[119],"i.e.,":[120],"Res-CNN,":[121],"LSTM-FCN":[122],"InceptionTime,":[124],"over":[126],"96%":[127],"accuracy,":[128],"three":[130],"classification":[131],"tasks,":[132],"including":[133],"textures":[134,143],"(13":[135],"types),":[136,139],"materials":[137,145],"(10":[138],"combinations":[141],"(18":[146],"types).":[147],"The":[148],"broad":[152],"applications":[153],"in":[154],"service":[155],"robots,":[156,159],"industrial":[157],"sorting":[158],"HMI.":[161]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
