Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition
Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world envir...
Saved in:
| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7324 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
27.09.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s22197324 |
Cover
| Abstract | Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains. |
|---|---|
| AbstractList | Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains. Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains. |
| Audience | Academic |
| Author | Bento, Nuno Barandas, Marília Cabitza, Federico Campagner, Andrea Gamboa, Hugo Carreiro, André V. Rebelo, Joana |
| AuthorAffiliation | 3 Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy 4 IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy 1 Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal 2 Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys–UNL), Departamento de Física, Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal |
| AuthorAffiliation_xml | – name: 1 Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal – name: 3 Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, 20126 Milan, Italy – name: 2 Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys–UNL), Departamento de Física, Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal – name: 4 IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy |
| Author_xml | – sequence: 1 givenname: Nuno orcidid: 0000-0001-7279-1890 surname: Bento fullname: Bento, Nuno – sequence: 2 givenname: Joana orcidid: 0000-0003-0385-053X surname: Rebelo fullname: Rebelo, Joana – sequence: 3 givenname: Marília orcidid: 0000-0002-9445-4809 surname: Barandas fullname: Barandas, Marília – sequence: 4 givenname: André V. orcidid: 0000-0002-4234-5336 surname: Carreiro fullname: Carreiro, André V. – sequence: 5 givenname: Andrea orcidid: 0000-0002-0027-5157 surname: Campagner fullname: Campagner, Andrea – sequence: 6 givenname: Federico orcidid: 0000-0002-4065-3415 surname: Cabitza fullname: Cabitza, Federico – sequence: 7 givenname: Hugo orcidid: 0000-0002-4022-7424 surname: Gamboa fullname: Gamboa, Hugo |
| BookMark | eNp9Uk1v1DAQjVAR_YAD_8ASF4q0bfyROLkgrba0W6kCCcE5mtjj4FViBydZtPx6nKaqaIWwD7bevHnPM-PT5Mh5h0nylqYXnJfp5cAYLSVn4kVyQgUTq4Kx9Oiv-3FyOgy7NGWc8-JVcsxzxnPB5Emy3_iuh2BdQ7bgtApgRtTkGmGcAg4kYuQKsSefcQrQkq_YRxjdCKP1biDGB3LlO7CO3KDDSLG_70MkItupA0fWarR7Ox5irvKNs3P0dfLSQDvgm4fzLPl-_enbZru6-3Jzu1nfrVSW0XFFMwqFrJmuM9CFKKDWhTbK5IIqUVAlZal0BozyWkvAHOqcpVLFZXRtipSfJbeLrvawq_pgOwiHyoOt7gEfmgrCaFWLVWGwZoJjVkgp6rQoQRssmVCpLPNasKj1YdGaXA-HX9C2j4I0reZBVI-DiOSPC7mf6g61ih2LvXnygqcRZ39Ujd9XZSYZEzQKvH8QCP7nhMNYdXZQ2Lbg0E9DxSTLaBnNZq93z6g7PwUX-zqzBKesFDPrYmE1EKu1zvjoq-LW2FkVP5SxEV9LkWeclulc7_mSoIIfhoDmv-VePuMqu3yRaGLbf2T8AS272uI |
| CitedBy_id | crossref_primary_10_1109_JSEN_2024_3356651 crossref_primary_10_3390_electronics12244924 crossref_primary_10_1016_j_robot_2023_104615 crossref_primary_10_3390_s23146511 crossref_primary_10_3390_s23010125 crossref_primary_10_3390_app13074175 crossref_primary_10_1016_j_eswa_2024_126295 crossref_primary_10_3390_w15061088 crossref_primary_10_1016_j_compmedimag_2024_102438 crossref_primary_10_3390_s23208449 crossref_primary_10_3390_s23125715 |
| Cites_doi | 10.1016/j.eswa.2016.04.032 10.1109/ACCESS.2020.3010715 10.1186/s12911-020-01224-9 10.1080/02664760802192981 10.1109/ISCE.2019.8901021 10.1016/j.neucom.2020.10.056 10.1109/SMC.2015.263 10.1016/j.neucom.2020.09.091 10.12720/ijsps.1.2.256-262 10.1145/3537972.3537996 10.1145/2413097.2413148 10.3390/s22062360 10.3390/e20080592 10.1016/j.cmpb.2021.106288 10.1109/ACCESS.2018.2890675 10.1109/ISBI.2019.8759281 10.1186/1475-925X-14-S2-S6 10.3390/s21062141 10.3390/s21237853 10.1371/journal.pone.0075196 10.1088/1742-5468/ac3a74 10.1145/3410531.3414311 10.1109/ICSENS.2016.7808590 10.3390/s140610146 10.5220/0005699601900197 10.3390/s22093401 10.1016/j.softx.2020.100456 10.1109/PERCOM.2016.7456521 10.3390/s21186316 10.1109/ISWC.2012.13 10.1016/B978-1-78548-236-6.50002-7 10.1145/3265689.3265705 10.1109/SERVICES.2019.00084 10.3390/s21051669 10.3390/s19143213 10.1007/978-3-319-13105-4 10.1371/journal.pone.0237009 10.1016/j.patrec.2018.02.010 10.1109/ICITR51448.2020.9310792 10.3390/s19010057 10.3390/s18020679 10.1038/s42256-020-00257-z 10.1007/s10994-008-5059-5 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.3390/s22197324 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database (Proquest) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: BENPR name: AUTh Library subscriptions: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_8feb243e58774b089adfe924c0796b42 10.3390/s22197324 PMC9572241 A746531902 10_3390_s22197324 |
| GeographicLocations | Portugal Washington, D.C |
| GeographicLocations_xml | – name: Washington, D.C – name: Portugal |
| GrantInformation_xml | – fundername: FCT—Fundação para a Ciência e a Tecnologia grantid: E!114310 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM ADRAZ ADTOC IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c551t-151a87b2db5ad848abd8dfcf641c481c779cd5a213bd7ae6ab6207ccccfdbf803 |
| IEDL.DBID | M48 |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:35:47 EDT 2025 Sun Oct 26 03:24:33 EDT 2025 Tue Sep 30 17:19:07 EDT 2025 Thu Oct 02 10:40:38 EDT 2025 Tue Oct 07 07:35:36 EDT 2025 Mon Oct 20 16:50:14 EDT 2025 Thu Oct 16 04:42:13 EDT 2025 Thu Apr 24 22:53:58 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 19 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c551t-151a87b2db5ad848abd8dfcf641c481c779cd5a213bd7ae6ab6207ccccfdbf803 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4065-3415 0000-0002-0027-5157 0000-0002-4234-5336 0000-0001-7279-1890 0000-0003-0385-053X 0000-0002-9445-4809 0000-0002-4022-7424 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3390/s22197324 |
| PMID | 36236427 |
| PQID | 2724312944 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_8feb243e58774b089adfe924c0796b42 unpaywall_primary_10_3390_s22197324 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9572241 proquest_miscellaneous_2725199734 proquest_journals_2724312944 gale_infotracacademiconefile_A746531902 crossref_primary_10_3390_s22197324 crossref_citationtrail_10_3390_s22197324 |
| PublicationCentury | 2000 |
| PublicationDate | 20220927 |
| PublicationDateYYYYMMDD | 2022-09-27 |
| PublicationDate_xml | – month: 9 year: 2022 text: 20220927 day: 27 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | ref_50 Soleimani (ref_5) 2021; 426 ref_14 ref_58 ref_13 ref_56 ref_11 ref_55 ref_10 ref_54 Balcan (ref_19) 2008; 72 ref_51 ref_18 ref_17 ref_16 Gholamiangonabadi (ref_33) 2020; 8 Wang (ref_8) 2019; 119 Gretton (ref_52) 2012; 13 Xu (ref_31) 2019; 7 ref_24 ref_23 ref_22 ref_21 ref_28 Bousquet (ref_20) 2008; 35 ref_27 ref_26 Ahuja (ref_36) 2021; 34 ref_35 ref_34 ref_32 ref_30 Geirhos (ref_15) 2020; 2 ref_39 ref_38 ref_37 Barandas (ref_12) 2020; 11 Ronao (ref_29) 2016; 59 Cabitza (ref_53) 2020; 20 ref_47 ref_46 ref_45 ref_44 Cabitza (ref_25) 2021; 208 ref_43 ref_41 ref_40 ref_1 Shakya (ref_49) 2018; 8 ref_2 Ahmad (ref_3) 2013; 1 ref_48 ref_9 Shoaib (ref_42) 2014; 14 Nakkiran (ref_57) 2021; 2021 ref_4 ref_7 ref_6 |
| References_xml | – ident: ref_55 – ident: ref_26 – ident: ref_51 – volume: 59 start-page: 235 year: 2016 ident: ref_29 article-title: Human activity recognition with smartphone sensors using deep learning neural networks publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.04.032 – volume: 8 start-page: 133982 year: 2020 ident: ref_33 article-title: Deep neural networks for human activity recognition with wearable sensors: Leave-one-subject-out cross-validation for model selection publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010715 – volume: 20 start-page: 1 year: 2020 ident: ref_53 article-title: As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI publication-title: BMC Med. Inform. Decis. Mak. doi: 10.1186/s12911-020-01224-9 – volume: 35 start-page: 1011 year: 2008 ident: ref_20 article-title: Diagnostics of prior-data agreement in applied Bayesian analysis publication-title: J. Appl. Stat. doi: 10.1080/02664760802192981 – ident: ref_16 – ident: ref_18 doi: 10.1109/ISCE.2019.8901021 – volume: 426 start-page: 26 year: 2021 ident: ref_5 article-title: Cross-subject transfer learning in human activity recognition systems using generative adversarial networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.10.056 – ident: ref_13 doi: 10.1109/SMC.2015.263 – ident: ref_27 doi: 10.1016/j.neucom.2020.09.091 – volume: 34 start-page: 3438 year: 2021 ident: ref_36 article-title: Invariance principle meets information bottleneck for out-of-distribution generalization publication-title: Adv. Neural Inf. Process. Syst. – volume: 1 start-page: 256 year: 2013 ident: ref_3 article-title: Reviews on various inertial measurement unit (IMU) sensor applications publication-title: Int. J. Signal Process. Syst. doi: 10.12720/ijsps.1.2.256-262 – ident: ref_39 doi: 10.1145/3537972.3537996 – ident: ref_41 doi: 10.1145/2413097.2413148 – ident: ref_58 – ident: ref_34 doi: 10.3390/s22062360 – volume: 8 start-page: 577 year: 2018 ident: ref_49 article-title: Comparative study of machine learning and deep learning architecture for human activity recognition using accelerometer data publication-title: Int. J. Mach. Learn. Comput. – ident: ref_23 doi: 10.3390/e20080592 – volume: 208 start-page: 106288 year: 2021 ident: ref_25 article-title: The importance of being external. methodological insights for the external validation of machine learning models in medicine publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2021.106288 – volume: 7 start-page: 9893 year: 2019 ident: ref_31 article-title: InnoHAR: A deep neural network for complex human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2890675 – ident: ref_56 – ident: ref_10 – ident: ref_21 doi: 10.1109/ISBI.2019.8759281 – ident: ref_45 doi: 10.1186/1475-925X-14-S2-S6 – ident: ref_9 doi: 10.3390/s21062141 – ident: ref_17 – ident: ref_30 doi: 10.3390/s21237853 – ident: ref_43 doi: 10.1371/journal.pone.0075196 – volume: 2021 start-page: 124003 year: 2021 ident: ref_57 article-title: Deep double descent: Where bigger models and more data hurt publication-title: J. Stat. Mech. Theory Exp. doi: 10.1088/1742-5468/ac3a74 – ident: ref_7 doi: 10.1145/3410531.3414311 – ident: ref_14 doi: 10.1109/ICSENS.2016.7808590 – ident: ref_28 – volume: 14 start-page: 10146 year: 2014 ident: ref_42 article-title: Fusion of smartphone motion sensors for physical activity recognition publication-title: Sensors doi: 10.3390/s140610146 – ident: ref_48 doi: 10.5220/0005699601900197 – ident: ref_2 doi: 10.3390/s22093401 – ident: ref_11 – volume: 11 start-page: 100456 year: 2020 ident: ref_12 article-title: TSFEL: Time series feature extraction library publication-title: SoftwareX doi: 10.1016/j.softx.2020.100456 – ident: ref_46 doi: 10.1109/PERCOM.2016.7456521 – ident: ref_32 doi: 10.3390/s21186316 – ident: ref_40 doi: 10.1109/ISWC.2012.13 – ident: ref_22 doi: 10.1016/B978-1-78548-236-6.50002-7 – ident: ref_6 doi: 10.1145/3265689.3265705 – ident: ref_37 – ident: ref_47 doi: 10.1109/SERVICES.2019.00084 – ident: ref_38 doi: 10.3390/s21051669 – ident: ref_1 doi: 10.3390/s19143213 – ident: ref_44 doi: 10.1007/978-3-319-13105-4 – volume: 13 start-page: 723 year: 2012 ident: ref_52 article-title: A kernel two-sample test publication-title: J. Mach. Learn. Res. – ident: ref_24 doi: 10.1371/journal.pone.0237009 – volume: 119 start-page: 3 year: 2019 ident: ref_8 article-title: Deep learning for sensor-based activity recognition: A survey publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2018.02.010 – ident: ref_54 – ident: ref_50 doi: 10.1109/ICITR51448.2020.9310792 – ident: ref_35 doi: 10.3390/s19010057 – ident: ref_4 doi: 10.3390/s18020679 – volume: 2 start-page: 665 year: 2020 ident: ref_15 article-title: Shortcut learning in deep neural networks publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-020-00257-z – volume: 72 start-page: 89 year: 2008 ident: ref_19 article-title: A theory of learning with similarity functions publication-title: Mach. Learn. doi: 10.1007/s10994-008-5059-5 |
| SSID | ssj0023338 |
| Score | 2.4825788 |
| Snippet | Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects,... |
| SourceID | doaj unpaywall pubmedcentral proquest gale crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 7324 |
| SubjectTerms | accelerometer Accuracy Adaptation Algorithms Datasets Deep learning domain generalization human activity recognition Hypotheses Neural networks Sensors |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LbxMxELZQL8AB8RSBUpmHBJdVs7Z37T2mL0VIcKio1Js1fixUSp2oSUD8e2Z2nVWigrg0R-_IcuZhf7Pr-YaxD0G5AKpFC4BWBTqFLKh8syhFaEqBAAFaejXw5Ws9vVCfL6vLrVZfdCespwfuFXdoWsz9lIyVQaDixqaB0EZMGvxYN7VT3e6Lo5tkKqdaEjOvnkdIYlJ_uBQYmFoKtXP6dCT9t7fi29cj76_TAn7_gtls6-w5e8weZdDIJ_1in7B7MT1lD7eoBJ-xn8d9Q8H0nU8hBX9Dzb8DJ4S3xoya4xg_iXHBiY0D5zrvbsDmwqO05Ihd-cn8Gq4Sz0zUuUCT40j3pp9PfN9pgp9vLh3N03N2cXb67Xha5J4KhUdstCrwgAejnQiugmCUARdMaH1bq9IrU3qtGx8qEKV0QUOswdVirD3-2uBaM5Yv2F6ap_iS8RZqg1MECZjVRB2bWMoyKFQ44D7SiBH7tNG19ZlwnPpezCwmHmQWO5hlxN4NooueZeNvQkdksEGAiLG7AXQXm93F_s9dRuwjmdtS-OJiPOQqBPxLRIRlJ5oI5xAloeT-xiNsjuulFRonR4ikcDVvh8cYkfSZBVKcrzuZiq7vSJTRO560s_TdJ-nqR8ft3VSaQNWIvR987t8aeXUXGnnNHggq6aAvbXqf7a1u1vENAq2VO-hi6g_KZyja priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELbK9gAcEE-xpSDzkOASdeM4cXJAaPvSCokVWlGpt2j8SFtpcZZ9FPHvmUmc0FWBHO2R5Xhm7Bl75hvG3lmpLcgKOQBKRigUSUTpm1EsbBELNBCgoquBL9NsciY_n6fnO2za5cJQWGW3JzYbta0N3ZEfCCXwrBOFlJ8WPyKqGkWvq10JDQilFezHBmLsDtsVhIw1YLuHJ9Ovs94FS9Aja_GFEnT2D1YCFVYlQm6dSg14_-0t-nbY5N2NX8CvnzCf3ziTTh-yB8GY5OOW-4_YjvOP2f0bEINP2PVRW2jQX_AJeGuWVBTccrL8Nuhpc2zjx84tOKF04FizJjI2JCT5FUeblh_X3-HK84BQHRI3ObY0LwB8bNoKFHzWBSPV_ik7Oz35djSJQq2FyOCCrSM8-CFXWlidgs1lDtrmtjJVJmMj89goVRibgogTbRW4DHQmRsrgV1ld5aPkGRv42rvnjFeQ5TiETQC9Hadc4eIkthIXHHB_KcSQfejWujQBiJzqYcxLdEiILWXPliF705MuWvSNvxEdEsN6AgLMbhrq5UUZ9K_MK6dRgFyao72rR3kBtnLoe5qRKjItcVLvid0lqTVOxkDITsBfIoCscqwIiA6tJ6Tc7ySiDPq-Kv9I55C97rtRU-n5BbyrNw1NSmE9CdKoLUnamvp2j7-6bDC_i1SRsTVkb3uZ-_eK7P1_ii_YPUFJHPS2pvbZYL3cuJdoWq31q6AvvwFS8Sb1 priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELagewAOvBGFBZmHBJdsGseJkxMqu6wqJFZoRaXlFPm5VBSnaptF8OuZSdyoZUFCIkdnYjnxePxNPPMNIS8NV0ZyBzMgBY9AKdII0zejhJkyYQAQpMNfAx9O8smUvz_Lzray-DGsElzxWWukMQsrgh1sFDMWJ2UsYPePF8a9uQj_kpIcNlSsSJdeJXt5Bmh8QPamJx_Hn9ukovB0RyiUgncfrxisUOxmZxtq2fov2-TLcZLXGr-QP77L-XxrEzq-ReRm-F3sydeDZq0O9M_fmB3_5_1uk5sBodJxp1J3yBXr75IbW7yF98jFYVe90J_TifRGL7HSuKEIJxtw3ym00SNrFxSpP6Cv0zbcNmQ5-RUFoEyP6m9y5mmgvQ7ZoBRa2mMFOtZdWQt6uolwqv19Mj1-9-lwEoUCDpEGILaOAE3IQihmVCZNwQupTGGcdjlPNC8SLUSpTSZZkiojpM2lytlIaLicUa4YpQ_IwNfePiTUybyALkwqwYWywpY2SRMDH6eUYLRKNiSvN_NZ6cBujkU25hV4OTj1VT_1Q_K8F110lB5_EnqLStELIAt321Avz6uwqKvCWcV4arMCQLQaFaU0zoJDq0eizBWHQb1ClarQVsBgtAwpD_BKyLpVjQWy2wEkA8n9jdZVwYisKiagc8BjHEbzrL8Nyx_PdKS3ddPKZBgrlIKM2NHWnaHv3vGzLy2ReJkJRHBD8qLX679_kUf_JPWYXGeYIILndmKfDNbLxj4B2LZWT8PK_AV1Wz6h priority: 102 providerName: Unpaywall |
| Title | Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition |
| URI | https://www.proquest.com/docview/2724312944 https://www.proquest.com/docview/2725199734 https://pubmed.ncbi.nlm.nih.gov/PMC9572241 https://www.mdpi.com/1424-8220/22/19/7324/pdf?version=1664336963 https://doaj.org/article/8feb243e58774b089adfe924c0796b42 |
| UnpaywallVersion | publishedVersion |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: HH5 dateStart: 20010101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: KQ8 dateStart: 20010101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: KQ8 dateStart: 20030101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: ABDBF dateStart: 20081201 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: ADMLS dateStart: 20081201 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: GX1 dateStart: 20010101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD : Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: RPM dateStart: 20030101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: AUTh Library subscriptions: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central Health & Medical Collection (via ProQuest) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 8FG dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1424-8220 dateEnd: 20250930 omitProxy: true ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M48 dateStart: 20030101 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZbxMxELZ6SFAeEKcIlMgcErxsyXq9a-8DQukRIqRGVUSk8LTytaVS6k1zAP33zOylRi0v5CEP3pHl9czY36w93xDy3nJtFc9BA0rwAIwiCjB9MwiZTUMGAEHl-GngdJQMJ_zbNJ5ukabGZj2ByztDO6wnNVnMDv5cXX8Bh_-MESeE7J-WDNxOADLYJruwQaVYweGUt4cJLIIwrCIV2hTfI_dg-Y4AgYuNXakk77-9RN--Nnl_7efq-reazW7sSYNH5GENJmm_0v5jsuX8E_LgBsXgU_LrqCo06M_pUHlrFlgU3FJEfmuItCm00WPn5hRZOqCvcXkztk5I8ksKmJYeF5fqwtOaobpO3KTQUp4A0L6pKlDQcXMZqfDPyGRw8v1oGNS1FgIDmGkVwMavpNDM6lhZyaXSVtrc5AkPDZehESI1NlYsjLQVyiVKJ6wnDPxyq3PZi56THV9494LQXCUSurCRgmjHCZe6MAoth7lXsL6krEM-NnOdmZqIHOthzDIISFBDWauhDnnbis4r9o27hA5RYa0AEmaXDcXiPKv9L5O504xHLpaAd3VPpsrmDmJP0xNpojkM6gOqO0NDg8EYVWcnwCshQVbWF0hEB-gJJPcbi8gac82YgM4BOnEYzZv2MXgqHr8o74p1KRPjtZ4IZMSGJW0MffOJv_hZcn6nsUCw1SHvWpv794y8_O_-X5E9hvkdeOwm9snOarF2rwF1rXSXbIupgH85-Nolu4cno7Nxt_yC0S29Ddomo7P-j7-VvDU8 |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZKORQOiKfYUsC8BJeoie3EyQGhpUu1pY9D1Up7S_1KqbQ4231Q9U_xG5nJq10VuDVHZ2Q5nvE84plvCHlvhbZKFMABJUUAQsEDLN8MImaziIGDoAr8NbB_kAyPxfdRPFohv9taGEyrbHVipahtafAf-SaTDGwdy4T4MjkPsGsU3q62LTRqsdh1lxcQss0-7wyAvx8Y2_52tDUMmq4CgQHvYB6AiVOp1MzqWNlUpErb1BamSERkRBoZKTNjY8Uirq1ULlE6YaE08BRWF2nIYd475K7goEvg_MjRVYDHId6r0Ys4z8LNGQN1IDkTSzavag1w0wDcTMpcW_iJurxQ4_E1i7f9kDxoXFXar2XrEVlx_jG5fw3A8An5tVW3MfSndKi8NVNsOW4p-pULiOMpjNGBcxOKGCAw12GVd9uUO_kZBY-ZDsqf6szTBv-6KQulMFLdL9C-qftb0MM21an0T8nxrez5M7LqS--eE1qoJIUpLFcQSznpMhfxyArYcAXaK2M98qnd69w0MOfYbWOcQ7iDbMk7tvTI2450UmN7_I3oKzKsI0A47mqgnJ7mzenO08JpEE8Xp-BN6zDNlC0cRLYmlFmiBSzqI7I7R6UBizGqqX2AT0L4rbwvEeYOfDOg3GglIm-0ySy_kv0eedO9Bj2AlzvKu3JR0cSYNMSBRi5J0tLSl9_4sx8VongWS3TleuRdJ3P_3pH1_y_xNVkbHu3v5Xs7B7svyD2G5SJ4iyc3yOp8unAvwYmb61fVyaHk5LaP6h8I7l_y |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ZbxMxELZKkTgeEKcIFDCX6Msqu17v2vuAUGiIUgoVqqiUt8XXlkrBG3JQ9a_x65jZq40KvDWP9shyPIdn1jPfEPLKcm0VL4ADSvAAhCIOsHwziJjNIgYOgirw08Dn_XR8yD9OkskG-d3WwmBaZWsTK0NtS4PfyPtMMLjrWMZ5v2jSIr4MR-9mPwPsIIUvrW07jVpE9tzpCYRvi7e7Q-D1a8ZGH77ujIOmw0BgwFNYBnDdKSk0szpRVnKptJW2MEXKI8NlZITIjE0Ui2JthXKp0ikLhYFfYXUhwxjWvUKuijjOMJ1QTM6CvRhivxrJCCbD_oKBaRAx42v3X9Um4OJlcDFB8_rKz9TpiZpOz91-o9vkVuO20kEtZ3fIhvN3yc1zYIb3yK-duqWhP6Jj5a2ZY_txS9HHXEFMT2GMDp2bUcQDgbUOqhzcpvTJLyh4z3RY_lDHnjZY2E2JKIWR6q2BDkzd64IetGlPpb9PDi_lzB-QTV9695DQQqUSlrCxgrjKCZe5KI4shwNXYMky1iPb7VnnpoE8x84b0xxCH2RL3rGlR150pLMa5-NvRO-RYR0BQnNXA-X8KG80PZeF0yCqLpHgWetQZsoWDqJcE4os1Rw29QbZnaMBgc0Y1dRBwF9CKK58IBDyDvw0oNxqJSJvLMsiP9ODHnneTYNNwIce5V25qmgSTCCKgUasSdLa1tdn_PH3Cl08SwS6dT3yspO5f5_Io_9v8Rm5Bkqaf9rd33tMbjCsHMEHPbFFNpfzlXsC_txSP60Uh5Jvl62pfwBFlGQ1 |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELagewAOvBGFBZmHBJdsGseJkxMqu6wqJFZoRaXlFPm5VBSnaptF8OuZSdyoZUFCIkdnYjnxePxNPPMNIS8NV0ZyBzMgBY9AKdII0zejhJkyYQAQpMNfAx9O8smUvz_Lzray-DGsElzxWWukMQsrgh1sFDMWJ2UsYPePF8a9uQj_kpIcNlSsSJdeJXt5Bmh8QPamJx_Hn9ukovB0RyiUgncfrxisUOxmZxtq2fov2-TLcZLXGr-QP77L-XxrEzq-ReRm-F3sydeDZq0O9M_fmB3_5_1uk5sBodJxp1J3yBXr75IbW7yF98jFYVe90J_TifRGL7HSuKEIJxtw3ym00SNrFxSpP6Cv0zbcNmQ5-RUFoEyP6m9y5mmgvQ7ZoBRa2mMFOtZdWQt6uolwqv19Mj1-9-lwEoUCDpEGILaOAE3IQihmVCZNwQupTGGcdjlPNC8SLUSpTSZZkiojpM2lytlIaLicUa4YpQ_IwNfePiTUybyALkwqwYWywpY2SRMDH6eUYLRKNiSvN_NZ6cBujkU25hV4OTj1VT_1Q_K8F110lB5_EnqLStELIAt321Avz6uwqKvCWcV4arMCQLQaFaU0zoJDq0eizBWHQb1ClarQVsBgtAwpD_BKyLpVjQWy2wEkA8n9jdZVwYisKiagc8BjHEbzrL8Nyx_PdKS3ddPKZBgrlIKM2NHWnaHv3vGzLy2ReJkJRHBD8qLX679_kUf_JPWYXGeYIILndmKfDNbLxj4B2LZWT8PK_AV1Wz6h |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparing+Handcrafted+Features+and+Deep+Neural+Representations+for+Domain+Generalization+in+Human+Activity+Recognition&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Bento%2C+Nuno&rft.au=Rebelo%2C+Joana&rft.au=Barandas%2C+Mar%C3%ADlia&rft.au=Carreiro%2C+Andr%C3%A9+V.&rft.date=2022-09-27&rft.pub=MDPI&rft.eissn=1424-8220&rft.volume=22&rft.issue=19&rft_id=info:doi/10.3390%2Fs22197324&rft_id=info%3Apmid%2F36236427&rft.externalDocID=PMC9572241 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |