Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction

Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm buil...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Annals of biomedical engineering Ročník 51; číslo 7; s. 1471 - 1484
Hlavní autori:	Yu, Shuangyue, Yang, Jianfu, Huang, Tzu-Hao, Zhu, Junxi, Visco, Christopher J., Hameed, Farah, Stein, Joel, Zhou, Xianlian, Su, Hao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.07.2023 Springer Nature B.V
Predmet:	Algorithms Artificial neural networks Biochemistry Biological and Medical Physics Biomechanical Phenomena Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Classical Mechanics Controllers Gait Humans Lower Extremity Movement Disorders Neural networks Neural Networks, Computer Original Article Real time Thigh Walking Wearable computers Wearable technology Gait phase detection Exoskeleton Activities classification Artificial neural networks
ISSN:	0090-6964, 1573-9686, 1573-9686
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ( rRMSE 0 : 6.3%) and predict 200-ms-ahead ( rRMSE 200 ms : 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.
AbstractList	Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate (rRMSE0: 6.3%) and predict 200-ms-ahead (rRMSE200ms: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices. Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ( rRMSE 0 : 6.3%) and predict 200-ms-ahead ( rRMSE 200 ms : 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices. Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ([Formula: see text]: 6.3%) and predict 200-ms-ahead ([Formula: see text]: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ([Formula: see text]: 6.3%) and predict 200-ms-ahead ([Formula: see text]: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices. Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ([Formula: see text]: 6.3%) and predict 200-ms-ahead ([Formula: see text]: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.
Author	Hameed, Farah Zhu, Junxi Su, Hao Yang, Jianfu Visco, Christopher J. Stein, Joel Zhou, Xianlian Huang, Tzu-Hao Yu, Shuangyue
Author_xml	– sequence: 1 givenname: Shuangyue surname: Yu fullname: Yu, Shuangyue organization: Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University – sequence: 2 givenname: Jianfu surname: Yang fullname: Yang, Jianfu organization: Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University – sequence: 3 givenname: Tzu-Hao surname: Huang fullname: Huang, Tzu-Hao organization: Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University – sequence: 4 givenname: Junxi surname: Zhu fullname: Zhu, Junxi organization: Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University – sequence: 5 givenname: Christopher J. surname: Visco fullname: Visco, Christopher J. organization: Department of Rehabilitation and Regenerative Medicine, Columbia University Vagelos College of Physicians and Surgeons – sequence: 6 givenname: Farah surname: Hameed fullname: Hameed, Farah organization: Department of Rehabilitation and Regenerative Medicine, Columbia University Vagelos College of Physicians and Surgeons – sequence: 7 givenname: Joel surname: Stein fullname: Stein, Joel organization: Department of Rehabilitation and Regenerative Medicine, Columbia University Vagelos College of Physicians and Surgeons – sequence: 8 givenname: Xianlian surname: Zhou fullname: Zhou, Xianlian organization: Department of Biomedical Engineering, New Jersey Institute of Technology – sequence: 9 givenname: Hao orcidid: 0000-0003-3299-7418 surname: Su fullname: Su, Hao email: hao.su796@ncsu.edu organization: Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36681749$$D View this record in MEDLINE/PubMed
BookMark	eNp9kctuFDEQRa0oKJkM_EAWqCU2LGJw2e3XcjLKS4ogC1gbt9sNDj3dwXaD5u_xPJJIWWRVKuvcctW9J-hwGAeP0CmQT0CI_JyA1ExjQhkmDDjg9QGaAZcMa6HEIZoRogkWWtTH6CSle0IAFONH6JgJoUDWeoZ-LGIOXXDB9tUXP8Vtyf_G-Buf2-TbauFy-Bty8Kla9jalDWxzGIez6sqGXN39Klh1kXJY7Z_t0FZ30bfBbfq36E1n--Tf7escfb-8-La8xrdfr26Wi1vsmOQZN5SBpB1lXhLNuK-1Bc47UI2H2lJh68bVDVjfdFIQ1jAgTrlWSd1BTZljc_RxN_chjn8mn7JZheR839vBj1MyVApFmdbACvrhBXo_TnEo2xmqKOeK6-LTHL3fU1Oz8q15iOXCuDaP3hWA7gAXx5Si754QIGYTkNkFZEpAZhuQWReReiFyIW-Ny9GG_nUp20lT-Wf46ePz2q-o_gO24KQw
CitedBy_id	crossref_primary_10_1109_ACCESS_2023_3297724 crossref_primary_10_3390_s23063032 crossref_primary_10_1109_TNSRE_2025_3595496 crossref_primary_10_1007_s11063_023_11324_y crossref_primary_10_3390_s25144302 crossref_primary_10_1088_2516_1091_ada333 crossref_primary_10_1109_JSEN_2024_3406596 crossref_primary_10_1109_TASE_2025_3545899 crossref_primary_10_1109_TIM_2025_3548067 crossref_primary_10_1109_ACCESS_2025_3580867 crossref_primary_10_1109_TIM_2025_3580898 crossref_primary_10_1109_JBHI_2024_3380099 crossref_primary_10_1007_s42235_025_00723_7 crossref_primary_10_1109_ACCESS_2024_3414175 crossref_primary_10_1109_JBHI_2025_3561380 crossref_primary_10_3390_s24196318
Cites_doi	10.1109/TBME.2021.3065809 10.1016/j.neucom.2019.06.081 10.1109/TBME.2008.2003293 10.1089/soro.2016.0030 10.1109/LRA.2017.2734239 10.1126/science.aav7536 10.1109/JBHI.2018.2865218 10.1038/s41598-021-94449-1 10.1109/TNSRE.2020.2987155 10.3390/s18020394 10.1109/TRO.2020.3005533 10.1109/TBME.2011.2161671 10.3390/s19132988 10.1109/TBME.2017.2750139 10.3390/s151229858 10.1109/TNSRE.2018.2819508 10.1371/journal.pone.0056137 10.1016/j.jbiomech.2011.01.030 10.1109/LRA.2019.2931427 10.1126/science.aal5054 10.1038/s41598-018-22676-0 10.1115/1.4029336 10.1038/323533a0 10.3389/frobt.2018.00078 10.1109/TMRB.2020.3021132 10.1109/TMRB.2019.2961749 10.1109/IROS.2015.7354129 10.1109/ICORR.2019.8779554 10.1115/DMD2019-3266 10.1109/ICRA.2018.8460841 10.1109/BioRob49111.2020.9224359 10.1145/3219819.3220124
ContentType	Journal Article
Copyright	The Author(s) under exclusive licence to Biomedical Engineering Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
Copyright_xml	– notice: The Author(s) under exclusive licence to Biomedical Engineering Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 7X7 7XB 88E 8AO 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K9. KR7 L6V L7M LK8 L~C L~D M0S M1P M7P M7S P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PTHSS 7X8
DOI	10.1007/s10439-023-03151-y
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Database (Proquest) ProQuest Central Technology collection Natural Science Collection ProQuest One ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) 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 Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering collection MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection Materials Business File ProQuest One Applied & Life Sciences ProQuest One Sustainability Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Materials Science & Engineering Collection Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Materials Research Database MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Engineering
EISSN	1573-9686
EndPage	1484
ExternalDocumentID	36681749 10_1007_s10439_023_03151_y
Genre	Journal Article
GrantInformation_xml	– fundername: National Science Foundation grantid: CMMI 1944655; 2026622 funderid: http://dx.doi.org/10.13039/100000001 – fundername: National Institute on Disability, Independent Living, and Rehabilitation Research grantid: 90DPGE0011 funderid: http://dx.doi.org/10.13039/100009157 – fundername: National Institutes of Health grantid: R01EB029765 funderid: http://dx.doi.org/10.13039/100000002 – fundername: ACL HHS grantid: 90DPGE0011 – fundername: NIH HHS grantid: R01EB029765
GroupedDBID	--- -4W -56 -5G -BR -DZ -EM -Y2 -~C -~X .86 .GJ .VR 06C 06D 0R~ 0VY 199 1N0 1SB 2.D 203 23M 28- 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3SX 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5RE 5VS 67N 67Z 6J9 6NX 78A 7X7 85S 88E 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADYPR ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN B-. BA0 BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP EBD EBLON EBS EIOEI EJD EMOBN EN4 EPAXT ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW KPH L6V L7B LAK LK8 LLZTM M1P M4Y M7P M7S MA- MK~ ML~ N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 PF0 PQQKQ PROAC PSQYO PT4 PT5 PTHSS Q2X QOK QOR QOS R4E R89 R9I RHV RNI RNS ROL RPX RRX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3A S3B SAP SBL SBY SCLPG SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZN T13 T16 TEORI TN5 TSG TSK TSV TUC TUS U2A U9L UG4 UKHRP UKR UOJIU UTJUX UZXMN VC2 VFIZW VH1 W23 W48 WH7 WJK WK6 WK8 YLTOR Z45 Z7R Z7S Z7U Z7V Z7W Z7X Z7Y Z7Z Z81 Z82 Z83 Z87 Z88 Z8M Z8N Z8O Z8R Z8T Z8V Z8W Z91 Z92 ZGI ZMTXR ZOVNA ZY4 ~EX ~KM AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 7XB 8BQ 8FD 8FK AZQEC DWQXO F28 FR3 GNUQQ H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D P64 PKEHL PQEST PQUKI 7X8 PUEGO
ID	FETCH-LOGICAL-c375t-b23172f23e70935e49a155f18be14a26a4bc4b1aebf7603b310c8cd879f1423c3
IEDL.DBID	M7P
ISICitedReferencesCount	24
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000932319200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0090-6964 1573-9686
IngestDate	Thu Oct 02 10:10:32 EDT 2025 Wed Nov 05 14:51:53 EST 2025 Mon Jul 21 06:06:16 EDT 2025 Tue Nov 18 22:31:40 EST 2025 Sat Nov 29 01:37:17 EST 2025 Fri Feb 21 02:43:42 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	7
Keywords	Gait phase detection Exoskeleton Activities classification Artificial neural networks
Language	English
License	2023. The Author(s) under exclusive licence to Biomedical Engineering Society.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c375t-b23172f23e70935e49a155f18be14a26a4bc4b1aebf7603b310c8cd879f1423c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-3299-7418
PMID	36681749
PQID	2825585918
PQPubID	54090
PageCount	14
ParticipantIDs	proquest_miscellaneous_2768239913 proquest_journals_2825585918 pubmed_primary_36681749 crossref_primary_10_1007_s10439_023_03151_y crossref_citationtrail_10_1007_s10439_023_03151_y springer_journals_10_1007_s10439_023_03151_y
PublicationCentury	2000
PublicationDate	20230700 2023-07-00 2023-Jul 20230701
PublicationDateYYYYMMDD	2023-07-01
PublicationDate_xml	– month: 7 year: 2023 text: 20230700
PublicationDecade	2020
PublicationPlace	Cham
PublicationPlace_xml	– name: Cham – name: United States – name: New York
PublicationSubtitle	The Journal of the Biomedical Engineering Society
PublicationTitle	Annals of biomedical engineering
PublicationTitleAbbrev	Ann Biomed Eng
PublicationTitleAlternate	Ann Biomed Eng
PublicationYear	2023
Publisher	Springer International Publishing Springer Nature B.V
Publisher_xml	– name: Springer International Publishing – name: Springer Nature B.V
References	Sugar, Bates, Holgate, Kerestes, Mignolet, New, Ramachandran, Redkar, Wheeler (CR27) 2015; 7 Carcreff, Gerber, Paraschiv-Ionescu, De Coulon, Newman, Armand, Aminian (CR5) 2018; 18 Filli, Sutter, Easthope, Killeen, Meyer, Reuter, Lörincz, Bolliger, Weller, Curt (CR9) 2018; 8 Attal, Mohammed, Dedabrishvili, Chamroukhi, Oukhellou, Amirat (CR1) 2015; 15 CR18 Gao, Liu, Liang, Liao (CR10) 2020; 28 CR16 Swami, Lenhard, Kang (CR28) 2021; 11 Huang, Zhang, Hargrove, Dou, Rogers, Englehart (CR14) 2011; 58 CR11 Yu, Huang, Wang, Lynn, Sayd, Silivanov, Park, Tian, Su (CR32) 2019; 4 CR30 Choi, Park, Seo, Lee, Lee, Shim (CR6) 2017; 3 Elery, Rezazadeh, Nesler, Gregg (CR8) 2020; 36 Camargo, Flanagan, Csomay-Shanklin, Kanwar, Young (CR3) 2021; 68 Zhang, Fiers, Witte, Jackson, Poggensee, Atkeson, Collins (CR33) 2017; 356 Wang, Yan, Xiao (CR29) 2019; 31 Culver, Bartlett, Shultz, Goldfarb (CR7) 2018; 26 Huang, Kuiken, Lipschutz (CR13) 2008; 56 Hu, Rouse, Hargrove (CR12) 2018; 5 CR26 Kang, Kunapuli, Young (CR17) 2019; 2 CR25 Li, Derrode, Pieczynski (CR21) 2019; 362 Malcolm, Derave, Galle, De Clercq (CR22) 2013; 8 Rumelhart, Hinton, Williams (CR23) 1986; 323 Yap, Ng, Yeow (CR31) 2016; 3 Bartlett, Goldfarb (CR2) 2017; 65 Kim, Lee, Heimgartner, Revi, Karavas, Nathanson, Galiana, Eckert-Erdheim, Murphy, Perry (CR19) 2019; 365 Sánchez Manchola, Bernal, Munera, Cifuentes (CR24) 2019; 19 Hutabarat, Owaki, Hayashibe (CR15) 2020; 2 Caramia, Torricelli, Schmid, Muñoz-Gonzalez, Gonzalez-Vargas, Grandas, Pons (CR4) 2018; 22 Lewis, Ferris (CR20) 2011; 44 H Huang (3151_CR14) 2011; 58 TG Sugar (3151_CR27) 2015; 7 HL Bartlett (3151_CR2) 2017; 65 3151_CR26 P Malcolm (3151_CR22) 2013; 8 HK Yap (3151_CR31) 2016; 3 3151_CR25 J Zhang (3151_CR33) 2017; 356 MD Sánchez Manchola (3151_CR24) 2019; 19 H Li (3151_CR21) 2019; 362 J Camargo (3151_CR3) 2021; 68 L Filli (3151_CR9) 2018; 8 H Choi (3151_CR6) 2017; 3 DE Rumelhart (3151_CR23) 1986; 323 L Carcreff (3151_CR5) 2018; 18 H Huang (3151_CR13) 2008; 56 I Kang (3151_CR17) 2019; 2 S Culver (3151_CR7) 2018; 26 J Kim (3151_CR19) 2019; 365 CL Lewis (3151_CR20) 2011; 44 F Attal (3151_CR1) 2015; 15 3151_CR16 Y Hutabarat (3151_CR15) 2020; 2 3151_CR18 F Wang (3151_CR29) 2019; 31 3151_CR11 T Elery (3151_CR8) 2020; 36 3151_CR30 F Gao (3151_CR10) 2020; 28 B Hu (3151_CR12) 2018; 5 CP Swami (3151_CR28) 2021; 11 S Yu (3151_CR32) 2019; 4 C Caramia (3151_CR4) 2018; 22
References_xml	– ident: CR18 – volume: 68 start-page: 1569 issue: 5 year: 2021 end-page: 1578 ident: CR3 article-title: A machine learning strategy for locomotion classification and parameter estimation using fusion of wearable sensors publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2021.3065809 – volume: 362 start-page: 94 year: 2019 end-page: 105 ident: CR21 article-title: An adaptive and on-line IMU-based locomotion activity classification method using a triplet Markov model publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.06.081 – volume: 56 start-page: 65 issue: 1 year: 2008 end-page: 73 ident: CR13 article-title: A strategy for identifying locomotion modes using surface electromyography publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2008.2003293 – volume: 3 start-page: 144 issue: 3 year: 2016 end-page: 158 ident: CR31 article-title: High-force soft printable pneumatics for soft robotic applications publication-title: Soft Robot. doi: 10.1089/soro.2016.0030 – volume: 3 start-page: 411 issue: 1 year: 2017 end-page: 418 ident: CR6 article-title: A multifunctional ankle exoskeleton for mobility enhancement of gait-impaired individuals and seniors publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2017.2734239 – volume: 365 start-page: 668 issue: 6454 year: 2019 end-page: 672 ident: CR19 article-title: Reducing the metabolic rate of walking and running with a versatile, portable exosuit publication-title: Science doi: 10.1126/science.aav7536 – ident: CR16 – volume: 22 start-page: 1765 issue: 6 year: 2018 end-page: 1774 ident: CR4 article-title: IMU-based classification of Parkinson's disease from gait: a sensitivity analysis on sensor location and feature selection publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2018.2865218 – ident: CR30 – volume: 11 start-page: 1 issue: 1 year: 2021 end-page: 13 ident: CR28 article-title: A novel framework for designing a multi-DoF prosthetic wrist control using machine learning publication-title: Sci. Rep. doi: 10.1038/s41598-021-94449-1 – volume: 28 start-page: 1334 issue: 6 year: 2020 end-page: 1343 ident: CR10 article-title: IMU-based locomotion mode identification for transtibial prostheses, orthoses, and exoskeletons publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.2987155 – volume: 31 start-page: 3041 issue: 10 year: 2019 end-page: 3054 ident: CR29 article-title: Recognition of the gait phase based on new deep learning algorithm using multisensor information fusion publication-title: Sens. Mater. – volume: 18 start-page: 394 issue: 2 year: 2018 ident: CR5 article-title: What is the best configuration of wearable sensors to measure spatiotemporal gait parameters in children with cerebral palsy? publication-title: Sensors doi: 10.3390/s18020394 – volume: 36 start-page: 1649 issue: 6 year: 2020 end-page: 1668 ident: CR8 article-title: Design and validation of a powered knee–ankle prosthesis with high-torque, low-impedance actuators publication-title: IEEE Trans. Robot. doi: 10.1109/TRO.2020.3005533 – volume: 58 start-page: 2867 issue: 10 year: 2011 end-page: 2875 ident: CR14 article-title: Continuous locomotion-mode identification for prosthetic legs based on neuromuscular–mechanical fusion publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2011.2161671 – volume: 19 start-page: 2988 issue: 13 year: 2019 ident: CR24 article-title: Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals publication-title: Sensors doi: 10.3390/s19132988 – volume: 65 start-page: 1330 issue: 6 year: 2017 end-page: 1338 ident: CR2 article-title: A phase variable approach for IMU-based locomotion activity recognition publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2017.2750139 – ident: CR25 – volume: 15 start-page: 31314 issue: 12 year: 2015 end-page: 31338 ident: CR1 article-title: Physical human activity recognition using wearable sensors publication-title: Sensors doi: 10.3390/s151229858 – volume: 26 start-page: 993 issue: 5 year: 2018 end-page: 1002 ident: CR7 article-title: A stair ascent and descent controller for a powered ankle prosthesis publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2819508 – volume: 8 issue: 2 year: 2013 ident: CR22 article-title: A simple exoskeleton that assists plantarflexion can reduce the metabolic cost of human walking publication-title: PLoS ONE doi: 10.1371/journal.pone.0056137 – volume: 44 start-page: 789 issue: 5 year: 2011 end-page: 793 ident: CR20 article-title: Invariant hip moment pattern while walking with a robotic hip exoskeleton publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2011.01.030 – ident: CR11 – volume: 4 start-page: 4579 issue: 4 year: 2019 end-page: 4586 ident: CR32 article-title: Design and control of a high-torque and highly backdriveable hybrid soft exoskeleton for knee injury prevention during squatting publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2019.2931427 – volume: 356 start-page: 1280 issue: 6344 year: 2017 end-page: 1284 ident: CR33 article-title: Human-in-the-loop optimization of exoskeleton assistance during walking publication-title: Science doi: 10.1126/science.aal5054 – volume: 8 start-page: 1 issue: 1 year: 2018 end-page: 13 ident: CR9 article-title: Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time publication-title: Sci. Rep. doi: 10.1038/s41598-018-22676-0 – volume: 7 issue: 1 year: 2015 ident: CR27 article-title: Limit cycles to enhance human performance based on phase oscillators publication-title: J. Mech. Robot. doi: 10.1115/1.4029336 – volume: 323 start-page: 533 issue: 6088 year: 1986 end-page: 536 ident: CR23 article-title: Learning representations by back-propagating errors publication-title: Nature doi: 10.1038/323533a0 – ident: CR26 – volume: 5 start-page: 78 year: 2018 ident: CR12 article-title: Fusion of bilateral lower-limb neuromechanical signals improves prediction of locomotor activities publication-title: Front. Robot. AI doi: 10.3389/frobt.2018.00078 – volume: 2 start-page: 639 issue: 4 year: 2020 end-page: 648 ident: CR15 article-title: Quantitative gait assessment with feature-rich diversity using two IMU sensors publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2020.3021132 – volume: 2 start-page: 28 issue: 1 year: 2019 end-page: 37 ident: CR17 article-title: Real-time neural network-based gait phase estimation using a robotic hip exoskeleton publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2019.2961749 – volume: 36 start-page: 1649 issue: 6 year: 2020 ident: 3151_CR8 publication-title: IEEE Trans. Robot. doi: 10.1109/TRO.2020.3005533 – volume: 11 start-page: 1 issue: 1 year: 2021 ident: 3151_CR28 publication-title: Sci. Rep. doi: 10.1038/s41598-021-94449-1 – volume: 31 start-page: 3041 issue: 10 year: 2019 ident: 3151_CR29 publication-title: Sens. Mater. – volume: 26 start-page: 993 issue: 5 year: 2018 ident: 3151_CR7 publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2819508 – volume: 18 start-page: 394 issue: 2 year: 2018 ident: 3151_CR5 publication-title: Sensors doi: 10.3390/s18020394 – volume: 15 start-page: 31314 issue: 12 year: 2015 ident: 3151_CR1 publication-title: Sensors doi: 10.3390/s151229858 – ident: 3151_CR16 doi: 10.1109/IROS.2015.7354129 – volume: 28 start-page: 1334 issue: 6 year: 2020 ident: 3151_CR10 publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.2987155 – volume: 65 start-page: 1330 issue: 6 year: 2017 ident: 3151_CR2 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2017.2750139 – volume: 58 start-page: 2867 issue: 10 year: 2011 ident: 3151_CR14 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2011.2161671 – volume: 44 start-page: 789 issue: 5 year: 2011 ident: 3151_CR20 publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2011.01.030 – volume: 56 start-page: 65 issue: 1 year: 2008 ident: 3151_CR13 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2008.2003293 – volume: 3 start-page: 144 issue: 3 year: 2016 ident: 3151_CR31 publication-title: Soft Robot. doi: 10.1089/soro.2016.0030 – ident: 3151_CR26 doi: 10.1109/ICORR.2019.8779554 – volume: 323 start-page: 533 issue: 6088 year: 1986 ident: 3151_CR23 publication-title: Nature doi: 10.1038/323533a0 – volume: 3 start-page: 411 issue: 1 year: 2017 ident: 3151_CR6 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2017.2734239 – volume: 5 start-page: 78 year: 2018 ident: 3151_CR12 publication-title: Front. Robot. AI doi: 10.3389/frobt.2018.00078 – ident: 3151_CR30 doi: 10.1115/DMD2019-3266 – volume: 4 start-page: 4579 issue: 4 year: 2019 ident: 3151_CR32 publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2019.2931427 – volume: 68 start-page: 1569 issue: 5 year: 2021 ident: 3151_CR3 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2021.3065809 – volume: 2 start-page: 639 issue: 4 year: 2020 ident: 3151_CR15 publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2020.3021132 – volume: 2 start-page: 28 issue: 1 year: 2019 ident: 3151_CR17 publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2019.2961749 – volume: 362 start-page: 94 year: 2019 ident: 3151_CR21 publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.06.081 – ident: 3151_CR25 doi: 10.1109/ICRA.2018.8460841 – volume: 7 issue: 1 year: 2015 ident: 3151_CR27 publication-title: J. Mech. Robot. doi: 10.1115/1.4029336 – volume: 19 start-page: 2988 issue: 13 year: 2019 ident: 3151_CR24 publication-title: Sensors doi: 10.3390/s19132988 – ident: 3151_CR18 doi: 10.1109/BioRob49111.2020.9224359 – volume: 356 start-page: 1280 issue: 6344 year: 2017 ident: 3151_CR33 publication-title: Science doi: 10.1126/science.aal5054 – volume: 22 start-page: 1765 issue: 6 year: 2018 ident: 3151_CR4 publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2018.2865218 – ident: 3151_CR11 doi: 10.1145/3219819.3220124 – volume: 8 start-page: 1 issue: 1 year: 2018 ident: 3151_CR9 publication-title: Sci. Rep. doi: 10.1038/s41598-018-22676-0 – volume: 365 start-page: 668 issue: 6454 year: 2019 ident: 3151_CR19 publication-title: Science doi: 10.1126/science.aav7536 – volume: 8 issue: 2 year: 2013 ident: 3151_CR22 publication-title: PLoS ONE doi: 10.1371/journal.pone.0056137
SSID	ssj0011835
Score	2.5048783
Snippet	Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under...
SourceID	proquest pubmed crossref springer
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1471
SubjectTerms	Algorithms Artificial neural networks Biochemistry Biological and Medical Physics Biomechanical Phenomena Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Classical Mechanics Controllers Gait Humans Lower Extremity Movement Disorders Neural networks Neural Networks, Computer Original Article Real time Thigh Walking Wearable computers Wearable technology
SummonAdditionalLinks	– databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fT9swED6xMk3wAKxjI4whT9rbsIST1D8ey0ThYVQVsIm3zE4cgcQCasuk_vfcOUnbiW0Se4qSXGLLPvvudL7vA_hkClNIH2suCYEwVVJyh0EPjz06477URtoA4vpVDYf66sqMmqKwSXvavU1Jhp16qdgNjSdHG8OJmUDw2QtYRXOnibDh_OL7PHeASlrzFhgMjIxMm1KZP__jd3P0xMd8kh8NZmew-X8d3oKNxs1k_VovXsOKr7qwvgQ-2IVXZ01a_Q38ILkaSoIRWke4hOPh_AitXMH6eSCZwKiaBRZNEg5TesBO7M2Uja5RjB3jdvGzeWyrgo3G1ALdb8O3wfHll1PeUC_wPFG9KXfo9qm4jBOvKFPqU2PR8SiFdl6kNpY2dXnqhPWuVPIwcegk5jovtDKlQActT95Cp7qr_A6w0rrYeWWJ7Cp1SrhCl6Iok55T1h72XASinYEsb3DJiR7jNlsgKtNAZjiQWRjIbBbB5_k39zUqxz-l99qJzZoVOsmoZrdH4H06go_z17i2KGFiK3_3gDIYi1Htr0gieFcrxLy5REqN0ZyJ4KCd_cXP_96X3eeJv4e1OCgQnQ_eg850_OA_wMv81_RmMt4Pev8IfoP7DQ priority: 102 providerName: Springer Nature
Title	Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction
URI	https://link.springer.com/article/10.1007/s10439-023-03151-y https://www.ncbi.nlm.nih.gov/pubmed/36681749 https://www.proquest.com/docview/2825585918 https://www.proquest.com/docview/2768239913
Volume	51
WOSCitedRecordID	wos000932319200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-9686 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011835 issn: 0090-6964 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RFqFy4FEoDZRVkLhRizoPP06oRVs4wCpqAa24BNtx1Eol2-5ukfrvmXG8u6CKXrg4SjJJLM3YM5Oxvw_gtW50I3ymmCAEwkIKwSwmPSzzGIz7VmlhAojrJzkaqfFYV_GH2ywuq1zMiWGibiaO_pG_pT2WJYGtqXcXl4xYo6i6Gik01mCDUBKysHSvWlYR0Fx7BgONKZIWRdw0E7fOoStm6LEY8Rxwdv23Y7oRbd6olAYHdPTwf7v-CB7E0DM96G3lMdzx3Rbc_wOQcAvufY6l9ifwg-R6eImUEDzCISwZZ4fo-Zr0wAXiCcy008CsScJBzXvpB3M2T6tTFEuHOIX8jJdN16TVlL5A50_h69Hwy_uPLNIxMJfLcs4shoIya7PcS6qe-kIbDEZarqznhcmEKawrLDfetlLs5xYDR6dco6RuOQZtLt-G9W7S-R1IW2Mz66UhAqzCSm4b1fKmzUsrjdkvbQJ8oYvaRaxyosw4r1coy6S_GvVXB_3V1wm8WT5z0SN13Cq9u9BVHUftrF4pKoFXy9s43qiIYjo_uUIZzM9oPzDPE3jWm8byc7kQCjM8ncDewlZWL_93X57f3pcXsJkFO6U1wruwPp9e-Zdw1_2an82mA1iTYxlaNYCNw-GoOh6EURDaE2yr8ju2xyfffgM70gsI
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VgngceBQKhgKLBCe6Il6vd70HhAq0tGoa5VCkiou7a6_VSOCUJAXlT_EbmVnbCaiitx44WbEnthN_8_LsfAPw0pSmVF5kXBEDodRKcYdJDxceg3FfZUbZQOLa14NBdnRkhivwq-uFoWWVnU0MhrocF_SO_A31WKZEtpa9O_3OaWoUVVe7ERoNLPb9_CembNO3ex_x-b4SYmf78MMub6cK8CLR6Yw7jGi0qETiNRUBvTQWfWoVZ87H0gplpSuki613lVa9xGH8U2RFmWlTxRh7FAme9wpclVL0SIuG6ZdF1QLVo5mYYDAlM0q2TTptqx66fo4ektNchZjP_3aE56Lbc5XZ4PB27vxvf9VduN2G1myr0YV7sOLrNbj1B-HiGlw_aJcS3IdjkmvoMxgxlIRNWBLP36NnL9lWEQZrjPyUhcmhJBxgvMk-2dGMDU9QjG2jifzW7rZ1yYYTugJ9fgCfL-XXrsNqPa79I2CVdcJ5bWnAl3Q6dmVWxWWVpE5b20tdBHH37POi5WKnkSBf8yWLNOElR7zkAS_5PILXi--cNkwkF0pvdNjIW6s0zZfAiODF4jDaEyoS2dqPz1AG80_qd46TCB42UFxcLlEqwwzWRLDZYXN58n_fy-OL7-U53Ng9POjn_b3B_hO4KYKO0HroDVidTc78U7hW_JiNppNnQdsYHF82Zn8DJiJgcw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qBVVwYCmbocAgwYmO2vEyYx8QKrSBqiXyAaSqF3fGnhGRwClJCspf49fx3nicgCp664GTFed5SfzNW_yWD-BF0RSNtHHOJU0gTJWU3GDQw2OLzrh1eSG1H-J6qIbD_OioKFfgV98LQ2WVvU70iroZ1_SOfIt6LDMatpZvuVAWUe4O3px-58QgRZnWnk6jg8iBnf_E8G36en8Xn_XLOB7sfXr3gQeGAV4nKptxg96Nil2cWEUJQZsWGu2rE7mxItWx1KmpUyO0NU7J7cSgL1TndZOrwgn0Q-oEz3sFriqMMamcsMyOFxkMXCode0KB4Vkh09CwE9r20A3gaC05cSwIPv_bKJ7zdM9lab3xG9z6n_-223AzuNxsp1sjd2DFtutw449BjOuw9jGUGNyFE5LrxmowmlziN75Unr9Fi9-wndoTbozslHlGURL28N5k7_VoxsovKMb2UHV-C7t127ByQlegz_fg86X82vuw2o5b-xCY0yY2Vmki_kqNEqbJnWhckhml9XZmIhA9Dqo6zGgnqpCv1XK6NGGnQuxUHjvVPIJXi2NOuwklF0pv9DipgraaVkuQRPB88TXqGUoe6daOz1AG41LqgxZJBA86WC4ul0iZY2RbRLDZ43R58n_fy6OL7-UZrCFUq8P94cFjuB775UJl0huwOpuc2Sdwrf4xG00nT_3CY3By2ZD9DTy2aT0
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=Artificial+Neural+Network-Based+Activities+Classification%2C+Gait+Phase+Estimation%2C+and+Prediction&rft.jtitle=Annals+of+biomedical+engineering&rft.au=Yu%2C+Shuangyue&rft.au=Yang%2C+Jianfu&rft.au=Huang%2C+Tzu-Hao&rft.au=Zhu%2C+Junxi&rft.date=2023-07-01&rft.issn=1573-9686&rft.eissn=1573-9686&rft.volume=51&rft.issue=7&rft.spage=1471&rft_id=info:doi/10.1007%2Fs10439-023-03151-y&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0090-6964&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0090-6964&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0090-6964&client=summon