Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction
Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associate...
Gespeichert in:
| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 22; H. 20; S. 7960 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
MDPI AG
19.10.2022
MDPI |
| Schlagworte: | |
| ISSN: | 1424-8220, 1424-8220 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment. |
|---|---|
| AbstractList | Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment. Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect[sup.®] Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment. Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. We used the Kinect Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment. Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls.BACKGROUNDGait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls.We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation.METHODSWe used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation.The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%.RESULTSThe SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%.This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.CONCLUSIONSThis study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment. |
| Audience | Academic |
| Author | Little, Bryan E. Qi, Jin Chen, Biao Chen, Chaoyang Hu, Jie Sayeed, Zain Palacio-Lascano, Carlos Foote, Christopher Darwish, Muhammad Lou, Shenna Darwiche, Hussein F. |
| AuthorAffiliation | 3 South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA 2 Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA 1 State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China |
| AuthorAffiliation_xml | – name: 2 Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI 48201, USA – name: 1 State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China – name: 3 South Texas Health System—McAllen Department of Trauma, McAllen, TX 78503, USA |
| Author_xml | – sequence: 1 givenname: Biao surname: Chen fullname: Chen, Biao – sequence: 2 givenname: Chaoyang surname: Chen fullname: Chen, Chaoyang – sequence: 3 givenname: Jie surname: Hu fullname: Hu, Jie – sequence: 4 givenname: Zain surname: Sayeed fullname: Sayeed, Zain – sequence: 5 givenname: Jin orcidid: 0000-0002-4085-5041 surname: Qi fullname: Qi, Jin – sequence: 6 givenname: Hussein F. surname: Darwiche fullname: Darwiche, Hussein F. – sequence: 7 givenname: Bryan E. surname: Little fullname: Little, Bryan E. – sequence: 8 givenname: Shenna surname: Lou fullname: Lou, Shenna – sequence: 9 givenname: Muhammad surname: Darwish fullname: Darwish, Muhammad – sequence: 10 givenname: Christopher surname: Foote fullname: Foote, Christopher – sequence: 11 givenname: Carlos surname: Palacio-Lascano fullname: Palacio-Lascano, Carlos |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36298311$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkktv1DAQxyNURB9w4AugSFzgkNaPJLYvSGXFlkqLqBBwQrImfqReJfZiJ0h8exy2rNoK-WB75jd_z3jmtDjywZuieInROaUCXSRCCGKiRU-KE1yTuuL5fnTvfFycprRFiFBK-bPimLZEcIrxSfFjFcbdPJlYfnfJBV-C1-UnULfOm3JjIHrn--o9JKPLK3BTeQNTpn35xajQezctMTbEcj3AVK5hGMqbaLRTi-N58dTCkMyLu_2s-Lb-8HX1sdp8vrpeXW4q1SA-VcIyDZx3lrVcY62AKN6ihihhATBjQlHFmKo73XCsTdt0mLWmFhiLlnQa0bPieq-rA2zlLroR4m8ZwMm_hhB7CXFyajASt5hzVlOrbFZQ-RcaA8LwrhO20RZnrXd7rd3cjUYr46cIwwPRhx7vbmUffknRYkIbmgXe3AnE8HM2aZKjS8oMA3gT5iQJI6LBDNM6o68fodswR5-_aqF4QwlqFup8T_WQC3Dehvyuykub0ak8CdZl-yWr2xwgsMgBr-6XcMj9X9cz8HYPqBhSisYeEIzkMlHyMFGZvXjEKjfB0t2chRv-E_EH7RvLnA |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2024_106771 crossref_primary_10_3390_healthcare13121405 crossref_primary_10_54370_ordubtd_1658926 crossref_primary_10_1038_s41598_025_85934_y crossref_primary_10_1080_17582024_2025_2510842 crossref_primary_10_1097_NR9_0000000000000026 crossref_primary_10_1109_ACCESS_2025_3575023 crossref_primary_10_3390_s24237651 crossref_primary_10_3390_s25020498 crossref_primary_10_1371_journal_pone_0317933 crossref_primary_10_3390_jimaging10120326 crossref_primary_10_1038_s41598_024_65633_w crossref_primary_10_1007_s40520_023_02532_6 crossref_primary_10_4108_eetpht_11_9094 crossref_primary_10_1007_s40009_025_01680_9 crossref_primary_10_1007_s11277_024_11116_0 crossref_primary_10_1016_j_bspc_2025_107494 crossref_primary_10_1007_s11042_024_19673_z crossref_primary_10_3389_fnins_2025_1493988 crossref_primary_10_3390_healthcare11091258 crossref_primary_10_1109_TNSRE_2025_3577550 crossref_primary_10_1515_jom_2024_0158 crossref_primary_10_3390_bioengineering11060548 crossref_primary_10_3390_s23229194 crossref_primary_10_3390_jfmk10010073 crossref_primary_10_1016_j_inffus_2025_103175 crossref_primary_10_3389_fbioe_2025_1645162 crossref_primary_10_1109_ACCESS_2024_3368065 crossref_primary_10_3390_s25092755 crossref_primary_10_1109_JIOT_2024_3429516 crossref_primary_10_3390_app14093874 crossref_primary_10_1109_JSEN_2024_3446673 crossref_primary_10_3390_app14020558 crossref_primary_10_3390_robotics12020044 crossref_primary_10_1109_ACCESS_2025_3602153 |
| Cites_doi | 10.1016/S1050-6411(00)00038-9 10.1109/JSEN.2022.3197235 10.1007/s11042-021-11217-z 10.1109/TITB.2009.2022913 10.1159/000520732 10.1109/ACCESS.2020.2978090 10.1007/PL00013831 10.1016/j.cmpb.2014.09.005 10.1038/s41598-020-61423-2 10.1093/gerona/glu225 10.2522/ptj.20100391 10.1016/j.cmpb.2020.105721 10.1080/00325481.1993.11701693 10.15585/mmwr.mm6927a5 10.1007/s13244-018-0639-9 10.1080/03091902.2020.1822940 10.1145/3065386 10.1016/j.gaitpost.2013.05.003 10.1109/TKDE.2009.191 10.1056/NEJM198812293192604 10.1123/japa.2017-0225 10.1109/TMI.2016.2528162 10.1186/1471-2105-8-S4-S2 10.1109/TNSRE.2017.2736939 10.23919/SpringSim.2019.8732857 10.1186/s12984-019-0486-z 10.1016/j.jsr.2016.05.001 10.3390/s22113991 10.3390/s22093155 10.1007/s11517-018-1795-2 10.1186/s40537-021-00444-8 10.1080/00031305.1992.10475879 10.1161/STROKEAHA.110.586917 10.1109/COMPSAC54236.2022.00037 10.3390/s20185104 10.3390/s22030824 10.3390/s16122090 10.3390/s17010006 10.3390/s22114242 10.3390/s22134910 10.3390/info10010003 10.1111/jgs.15304 10.3390/s22103700 10.1109/ASRU.2013.6707749 10.3389/frobt.2021.749274 10.3390/s16010066 10.1186/s12938-021-00898-0 10.1016/j.clinbiomech.2014.03.013 10.1016/j.compbiomed.2019.103366 10.1088/1361-6560/ab78bf 10.1016/j.jstrokecerebrovasdis.2020.105035 10.1016/j.jstrokecerebrovasdis.2021.106011 10.1007/s11517-008-0327-x 10.3389/fneur.2021.666458 10.1007/s00415-022-11251-3 10.1109/ACCESS.2018.2810849 10.1007/s12199-010-0154-1 10.5435/00124635-200702000-00005 10.1145/130385.130401 10.1016/j.bspc.2021.102577 10.5603/PJNNS.a2021.0084 10.3390/s17030478 10.1016/j.gaitpost.2021.08.016 10.3390/s21165437 10.3390/s22155482 10.1016/j.ins.2019.06.039 10.3390/s19071644 10.1145/3436369.3437434 10.1038/s41598-021-00458-5 10.1186/1475-925X-10-99 10.1016/j.compbiomed.2019.01.009 10.1109/JBHI.2021.3092875 10.3390/s22166282 10.1371/journal.pone.0192345 10.7888/juoeh.36.41 10.1016/j.artmed.2018.12.007 10.30534/ijeter/2020/38862020 10.3390/s21082866 10.1097/PHH.0000000000000816 10.3390/s21175749 10.3390/s21155134 10.1177/14604582211055650 10.1109/JTEHM.2020.2998326 10.1016/j.jbiomech.2019.02.026 |
| 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 CGR CUY CVF ECM EIF NPM 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 DOA |
| DOI | 10.3390/s22207960 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest - Health & Medical Complete保健、医学与药学数据库 ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials - QC Proquest Central ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic ProQuest - Publicly Available Content Database 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 (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) 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 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: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_16188743fcfe49c9835ea9e8bb9f5df1 PMC9612353 A746532919 36298311 10_3390_s22207960 |
| Genre | Journal Article |
| GeographicLocations | United States Massachusetts Washington Alaska United Kingdom--UK Florida United States--US |
| GeographicLocations_xml | – name: Massachusetts – name: Washington – name: United States – name: Alaska – name: United Kingdom--UK – name: Florida – name: United States--US |
| GrantInformation_xml | – fundername: Cross Fund for Medical and Engineering of Shanghai Jiao Tong University grantid: YG2021QN118 – fundername: National Social Science Foundation of China grantid: 17ZDA020 – fundername: Rehabilitation Institute of Michigan Foundation grantid: 22-2-003 – fundername: National Natural Science Foundation of China grantid: 51975360; 52035007 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD 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 ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c508t-9f7da88bf768d1dca2c86052c9faa1779c3c77c4bd581de65b176e4911962bd03 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 41 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000875132800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:51:07 EDT 2025 Tue Nov 04 02:07:17 EST 2025 Thu Oct 02 07:14:54 EDT 2025 Tue Oct 07 07:21:36 EDT 2025 Tue Nov 04 18:17:18 EST 2025 Mon Jul 21 06:08:03 EDT 2025 Sat Nov 29 07:12:13 EST 2025 Tue Nov 18 21:25:50 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 20 |
| Keywords | support vector machine k nearest neighbor gait convolutional neural network long short-time memory machine learning fall recognition pattern recognition |
| 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/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c508t-9f7da88bf768d1dca2c86052c9faa1779c3c77c4bd581de65b176e4911962bd03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4085-5041 |
| OpenAccessLink | https://www.proquest.com/docview/2728532054?pq-origsite=%requestingapplication% |
| PMID | 36298311 |
| PQID | 2728532054 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_16188743fcfe49c9835ea9e8bb9f5df1 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9612353 proquest_miscellaneous_2729517134 proquest_journals_2728532054 gale_infotracacademiconefile_A746532919 pubmed_primary_36298311 crossref_primary_10_3390_s22207960 crossref_citationtrail_10_3390_s22207960 |
| PublicationCentury | 2000 |
| PublicationDate | 20221019 |
| PublicationDateYYYYMMDD | 2022-10-19 |
| PublicationDate_xml | – month: 10 year: 2022 text: 20221019 day: 19 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Zhou (ref_67) 2020; 10 Lai (ref_26) 2009; 13 Yamashita (ref_39) 2018; 9 ref_56 Pijnappels (ref_79) 2015; 70 ref_55 ref_54 ref_53 Zheng (ref_33) 2018; 6 ref_52 Wong (ref_37) 2016; 21 Zhou (ref_19) 2021; 68 Stanhope (ref_28) 2014; 29 Fujita (ref_84) 2020; 29 ref_61 ref_60 Geerars (ref_87) 2022; 91 Pan (ref_40) 2009; 22 Zakaria (ref_16) 2020; 8 Florence (ref_2) 2018; 66 Xu (ref_18) 2021; 20 Lonini (ref_10) 2022; 6 ref_69 Haddad (ref_3) 2019; 25 Melvin (ref_44) 2007; 8 Rubino (ref_89) 1993; 93 ref_23 ref_22 ref_20 ref_64 Burns (ref_76) 2016; 58 Lim (ref_88) 2007; 15 Zhao (ref_34) 2020; 8 ref_62 Gao (ref_74) 2019; 502 Tipton (ref_11) 2021; 55 Campanini (ref_86) 2013; 38 Kayhan (ref_57) 2021; 80 Khokhlova (ref_50) 2019; 94 Khera (ref_14) 2020; 44 Alzubaidi (ref_51) 2021; 8 Zhang (ref_65) 2019; 106 Imura (ref_66) 2021; 30 ref_72 ref_71 ref_70 Harris (ref_63) 2021; 8 Sacks (ref_78) 2018; 13 Naito (ref_85) 2014; 36 Prentice (ref_24) 2001; 11 Krizhevsky (ref_31) 2017; 60 ref_35 Shin (ref_41) 2016; 35 Joof (ref_47) 2019; 112 ref_32 ref_73 Yamada (ref_4) 2010; 15 Dolatabadi (ref_13) 2017; 25 Alaskar (ref_36) 2018; 9 Jiang (ref_21) 2020; 197 ref_38 Ng (ref_12) 2020; 8 Rossignol (ref_58) 2020; 65 Ardalan (ref_8) 2021; 27 Sulzer (ref_83) 2010; 41 Wu (ref_27) 2015; 2015 Chen (ref_17) 2011; 10 ref_80 Lau (ref_25) 2008; 46 Akbas (ref_29) 2019; 87 Smola (ref_42) 1998; 22 VanSwearingen (ref_81) 2011; 91 Fricke (ref_75) 2021; 12 ref_45 Kwolek (ref_30) 2014; 117 ref_43 Ortells (ref_15) 2018; 56 Lockhart (ref_82) 2021; 11 Ko (ref_7) 2018; 26 ref_49 ref_48 Cicirelli (ref_59) 2022; 26 ref_9 Altman (ref_46) 1992; 46 Tinetti (ref_77) 1988; 319 Farah (ref_68) 2019; 16 ref_5 Moreland (ref_1) 2020; 69 ref_6 |
| References_xml | – volume: 11 start-page: 19 year: 2001 ident: ref_24 article-title: Artificial neural network model for the generation of muscle activation patterns for human locomotion publication-title: J. Electromyogr. Kinesiol. doi: 10.1016/S1050-6411(00)00038-9 – ident: ref_56 doi: 10.1109/JSEN.2022.3197235 – volume: 80 start-page: 32763 year: 2021 ident: ref_57 article-title: Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-021-11217-z – volume: 13 start-page: 612 year: 2018 ident: ref_78 article-title: Multisociety Consensus Quality Improvement Revised Consensus Statement for Endovascular Therapy of Acute Ischemic Stroke publication-title: Int. J. Stroke – volume: 13 start-page: 687 year: 2009 ident: ref_26 article-title: Computational intelligence in gait research: A perspective on current applications and future challenges publication-title: IEEE Trans. Inf. Technol. Biomed doi: 10.1109/TITB.2009.2022913 – volume: 6 start-page: 9 year: 2022 ident: ref_10 article-title: Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study publication-title: Digit. Biomark. doi: 10.1159/000520732 – volume: 8 start-page: 44111 year: 2020 ident: ref_34 article-title: Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2978090 – volume: 22 start-page: 211 year: 1998 ident: ref_42 article-title: On a kernel-based method for pattern recognition, regression, approximation, and operator inversion publication-title: Algorithmica doi: 10.1007/PL00013831 – volume: 117 start-page: 489 year: 2014 ident: ref_30 article-title: Human fall detection on embedded platform using depth maps and wireless accelerometer publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2014.09.005 – volume: 10 start-page: 4426 year: 2020 ident: ref_67 article-title: The detection of age groups by dynamic gait outcomes using machine learning approaches publication-title: Sci. Rep. doi: 10.1038/s41598-020-61423-2 – volume: 70 start-page: 608 year: 2015 ident: ref_79 article-title: Ambulatory fall-risk assessment: Amount and quality of daily-life gait predict falls in older adults publication-title: J. Gerontol. A Biol. Sci. Med. Sci. doi: 10.1093/gerona/glu225 – volume: 91 start-page: 1740 year: 2011 ident: ref_81 article-title: Impact of Exercise to Improve Gait Efficiency on Activity and Participation in Older Adults with Mobility Limitations: A Randomized Controlled Trial publication-title: Phys. Ther. doi: 10.2522/ptj.20100391 – volume: 197 start-page: 105721 year: 2020 ident: ref_21 article-title: Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105721 – volume: 93 start-page: 185 year: 1993 ident: ref_89 article-title: Gait disorders in the elderly. Distinguishing between normal and dysfunctional gaits publication-title: Postgrad. Med. doi: 10.1080/00325481.1993.11701693 – volume: 69 start-page: 875 year: 2020 ident: ref_1 article-title: Trends in Nonfatal Falls and Fall-Related Injuries Among Adults Aged ≥65 Years—The United States, 2012–2018 publication-title: Morb. Mortal. Wkly. Rep. doi: 10.15585/mmwr.mm6927a5 – volume: 9 start-page: 611 year: 2018 ident: ref_39 article-title: Convolutional neural networks: An overview and application in radiology publication-title: Insights Imaging doi: 10.1007/s13244-018-0639-9 – volume: 44 start-page: 441 year: 2020 ident: ref_14 article-title: Role of machine learning in gait analysis: A review publication-title: J. Med. Eng. Technol. doi: 10.1080/03091902.2020.1822940 – volume: 60 start-page: 84 year: 2017 ident: ref_31 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – volume: 38 start-page: 165 year: 2013 ident: ref_86 article-title: A method to differentiate the causes of stiff-knee gait in stroke patients publication-title: Gait Posture doi: 10.1016/j.gaitpost.2013.05.003 – volume: 22 start-page: 1345 year: 2009 ident: ref_40 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – volume: 319 start-page: 1701 year: 1988 ident: ref_77 article-title: Risk factors for falls among elderly persons living in the community publication-title: N. Engl. J. Med. doi: 10.1056/NEJM198812293192604 – volume: 26 start-page: 577 year: 2018 ident: ref_7 article-title: Differential Gait Patterns by History of Falls and Knee Pain Status in Healthy Older Adults: Results from the Baltimore Longitudinal Study of Aging publication-title: J. Aging Phys. Act. doi: 10.1123/japa.2017-0225 – volume: 35 start-page: 1285 year: 2016 ident: ref_41 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528162 – volume: 8 start-page: S2 year: 2007 ident: ref_44 article-title: SVM-Fold: A tool for discriminative multi-class protein fold and superfamily recognition publication-title: BMC Bioinform. doi: 10.1186/1471-2105-8-S4-S2 – volume: 25 start-page: 2336 year: 2017 ident: ref_13 article-title: An Automated Classification of Pathological Gait Using Unobtrusive Sensing Technology publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2017.2736939 – ident: ref_38 doi: 10.23919/SpringSim.2019.8732857 – volume: 16 start-page: 22 year: 2019 ident: ref_68 article-title: Design, development, and evaluation of a local sensor-based gait phase recognition system using a logistic model decision tree for orthosis-control publication-title: J. Neuroeng. Rehabil. doi: 10.1186/s12984-019-0486-z – volume: 58 start-page: 99 year: 2016 ident: ref_76 article-title: The direct costs of fatal and non-fatal falls among older adults—United States publication-title: J. Saf. Res. doi: 10.1016/j.jsr.2016.05.001 – ident: ref_23 doi: 10.3390/s22113991 – ident: ref_62 doi: 10.3390/s22093155 – volume: 56 start-page: 1553 year: 2018 ident: ref_15 article-title: Vision-based gait impairment analysis for aided diagnosis publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-018-1795-2 – volume: 8 start-page: 53 year: 2021 ident: ref_51 article-title: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions publication-title: J. Big Data doi: 10.1186/s40537-021-00444-8 – volume: 46 start-page: 175 year: 1992 ident: ref_46 article-title: An introduction to kernel and nearest-neighbor nonparametric regression publication-title: Am. Stat. doi: 10.1080/00031305.1992.10475879 – volume: 41 start-page: 1709 year: 2010 ident: ref_83 article-title: Preswing knee flexion assistance is coupled with hip abduction in people with stiff-knee gait after stroke publication-title: Stroke doi: 10.1161/STROKEAHA.110.586917 – volume: 21 start-page: 4 year: 2016 ident: ref_37 article-title: Deep learning for health informatics publication-title: IEEE J. Biomed. Health Inform. – ident: ref_48 doi: 10.1109/COMPSAC54236.2022.00037 – ident: ref_52 doi: 10.3390/s20185104 – ident: ref_55 doi: 10.3390/s22030824 – ident: ref_60 doi: 10.3390/s16122090 – ident: ref_69 doi: 10.3390/s17010006 – ident: ref_70 doi: 10.3390/s22114242 – ident: ref_54 doi: 10.3390/s22134910 – ident: ref_6 doi: 10.3390/info10010003 – volume: 66 start-page: 693 year: 2018 ident: ref_2 article-title: Medical Costs of Fatal and Nonfatal Falls in Older Adults publication-title: J. Am. Geriatr. Soc. doi: 10.1111/jgs.15304 – volume: 9 start-page: 12 year: 2018 ident: ref_36 article-title: Deep learning-based model architecture for time-frequency images analysis publication-title: Int. J. Adv. Comput. Sci. Appl. – ident: ref_64 doi: 10.3390/s22103700 – volume: 2015 start-page: 528971 year: 2015 ident: ref_27 article-title: The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm publication-title: BioMed Res. Int. – ident: ref_35 doi: 10.1109/ASRU.2013.6707749 – volume: 8 start-page: 749274 year: 2021 ident: ref_63 article-title: A Survey of Human Gait-Based Artificial Intelligence Applications publication-title: Front. Robot. AI doi: 10.3389/frobt.2021.749274 – ident: ref_61 doi: 10.3390/s16010066 – volume: 20 start-page: 62 year: 2021 ident: ref_18 article-title: Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system publication-title: BioMedical Eng. OnLine doi: 10.1186/s12938-021-00898-0 – volume: 29 start-page: 518 year: 2014 ident: ref_28 article-title: Frontal plane compensatory strategies associated with self-selected walking speed in individuals post-stroke publication-title: Clin. Biomech. doi: 10.1016/j.clinbiomech.2014.03.013 – volume: 112 start-page: 103366 year: 2019 ident: ref_47 article-title: Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.103366 – volume: 65 start-page: 085013 year: 2020 ident: ref_58 article-title: Time-of-flight computed tomography-proof of principle publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ab78bf – volume: 29 start-page: 105035 year: 2020 ident: ref_84 article-title: Pedaling improves gait ability of hemiparetic patients with stiff-knee gait: Fall prevention during gait publication-title: J. Stroke Cereb. Dis. doi: 10.1016/j.jstrokecerebrovasdis.2020.105035 – volume: 30 start-page: 106011 year: 2021 ident: ref_66 article-title: Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis publication-title: J. Stroke. Cereb. Dis. doi: 10.1016/j.jstrokecerebrovasdis.2021.106011 – volume: 46 start-page: 563 year: 2008 ident: ref_25 article-title: Support vector machine for classification of walking conditions using miniature kinematic sensors publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-008-0327-x – volume: 12 start-page: 666458 year: 2021 ident: ref_75 article-title: Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders publication-title: Front. Neurol. doi: 10.3389/fneur.2021.666458 – ident: ref_80 doi: 10.1007/s00415-022-11251-3 – volume: 6 start-page: 15844 year: 2018 ident: ref_33 article-title: Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2810849 – volume: 15 start-page: 386 year: 2010 ident: ref_4 article-title: Predicting the probability of falls in community-dwelling elderly individuals using the trail-walking test publication-title: Environ. Health Prev. Med. doi: 10.1007/s12199-010-0154-1 – volume: 15 start-page: 107 year: 2007 ident: ref_88 article-title: Evaluation of the elderly patient with an abnormal gait publication-title: J. Am. Acad. Orthop. Surg. doi: 10.5435/00124635-200702000-00005 – ident: ref_45 doi: 10.1145/130385.130401 – volume: 68 start-page: 102577 year: 2021 ident: ref_19 article-title: Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2021.102577 – volume: 55 start-page: 513 year: 2021 ident: ref_11 article-title: Dissecting parkinsonism: Cognitive and gait disturbances publication-title: Neurol. Neurochir. Pol. doi: 10.5603/PJNNS.a2021.0084 – ident: ref_71 doi: 10.3390/s17030478 – volume: 91 start-page: 137 year: 2022 ident: ref_87 article-title: Treatment of knee hyperextension in post-stroke gait. A systematic review publication-title: Gait Posture doi: 10.1016/j.gaitpost.2021.08.016 – ident: ref_9 doi: 10.3390/s21165437 – ident: ref_22 doi: 10.3390/s22155482 – volume: 502 start-page: 279 year: 2019 ident: ref_74 article-title: Long short-term memory-based deep recurrent neural networks for target tracking publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.06.039 – ident: ref_32 doi: 10.3390/s19071644 – ident: ref_20 doi: 10.1145/3436369.3437434 – volume: 11 start-page: 20976 year: 2021 ident: ref_82 article-title: Prediction of fall risk among community-dwelling older adults using a wearable system publication-title: Sci. Rep. doi: 10.1038/s41598-021-00458-5 – volume: 10 start-page: 99 year: 2011 ident: ref_17 article-title: Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis publication-title: BioMedical Eng. OnLine doi: 10.1186/1475-925X-10-99 – volume: 106 start-page: 33 year: 2019 ident: ref_65 article-title: Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2019.01.009 – volume: 26 start-page: 229 year: 2022 ident: ref_59 article-title: Human Gait Analysis in Neurodegenerative Diseases: A Review publication-title: IEEE J Biomed Health Inf. doi: 10.1109/JBHI.2021.3092875 – ident: ref_43 – ident: ref_53 doi: 10.3390/s22166282 – ident: ref_73 doi: 10.1371/journal.pone.0192345 – volume: 36 start-page: 41 year: 2014 ident: ref_85 article-title: Quantification of gait using insole type foot pressure monitor: Clinical application for chronic hemiplegia publication-title: J. Uoeh. doi: 10.7888/juoeh.36.41 – volume: 94 start-page: 54 year: 2019 ident: ref_50 article-title: Normal and pathological gait classification LSTM model publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2018.12.007 – volume: 8 start-page: 2438 year: 2020 ident: ref_16 article-title: ASD Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysis publication-title: Int. J. Emerg. Trends Eng. Res. doi: 10.30534/ijeter/2020/38862020 – ident: ref_72 doi: 10.3390/s21082866 – volume: 25 start-page: 17-e24 year: 2019 ident: ref_3 article-title: Estimating the Economic Burden Related to Older Adult Falls by State publication-title: J. Public Health Manag. Pract. doi: 10.1097/PHH.0000000000000816 – ident: ref_49 doi: 10.3390/s21175749 – ident: ref_5 doi: 10.3390/s21155134 – volume: 27 start-page: 14604582211055650 year: 2021 ident: ref_8 article-title: Analysis of gait synchrony and balance in neurodevelopmental disorders using computer vision techniques publication-title: Health Inform. J. doi: 10.1177/14604582211055650 – volume: 8 start-page: 2100609 year: 2020 ident: ref_12 article-title: Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults with Dementia publication-title: IEEE J. Transl. Eng. Health Med. doi: 10.1109/JTEHM.2020.2998326 – volume: 87 start-page: 150 year: 2019 ident: ref_29 article-title: Hip circumduction is not a compensation for reduced knee flexion angle during gait publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2019.02.026 |
| SSID | ssj0023338 |
| Score | 2.5892289 |
| Snippet | Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation,... Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 7960 |
| SubjectTerms | Accuracy Aged Algorithms Artificial Intelligence Biometry Cameras Computer vision Computers convolutional neural network Data analysis Experiments Falls Fitness equipment Gait Geriatrics Humans long short-time memory Machine Learning Machine vision Neural networks Older people Pattern recognition Software Support Vector Machine Support vector machines Walking |
| SummonAdditionalLinks | – databaseName: DOAJ dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LixQxEC5k8aAH0fXVui5RBL00O510T5LjruzoxWUQlT0IIU8dGHplptffb1X64QwKXvbaSTeVenRVkaqvAF43VjohYlOib4hlrYMrtRKilHzOnRd-HlzKwybkxYW6vNTLnVFfVBPWwwP3jDshQHeFbi75FGvt8UNNtDoq53RqQsqJD0Y9YzI1pFoCM68eR0hgUn-yRS84kzrjUP7xPhmk_-9f8Y4v2q-T3HE8i_twb4gY2WlP6QO4FdtDuLuDI_gQvo2zGdjX3CrObBvYx1wmGdmAoPq9PEOHFdh7u-rYMqNqtuzTWD6E72D0yhZr27GFXa_ZckM3OLTwCL4szj-_-1AOYxNKj9FWV-okg1XKJcwkQhW85V5h0sK9TtZWUmoUgZS-dqHBYDXOG1fJOfK2QmPkLszEYzhor9r4FNisdjGpEKPXruazqHTtRBCBx6aySbgC3o7sNH7AFKfRFmuDuQVx3kycL-DVtPVnD6Txr01nJJNpA2Ff5weoEWbQCPM_jSjgDUnUkIUiMd4OjQZ4JMK6MqeSMOW4rnQBR6PQzWC6W8MlVzQto6kLeDkto9HRTYpt49V13oORKbXhFvCk15GJZowIkKQKyZB72rN3qP2VdvUjA3trwsJpxLOb4MJzuMOpU4OKb_QRHHSb6_gCbvtf3Wq7Oc7W8huTkhwQ priority: 102 providerName: Directory of Open Access Journals |
| Title | Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36298311 https://www.proquest.com/docview/2728532054 https://www.proquest.com/docview/2729517134 https://pubmed.ncbi.nlm.nih.gov/PMC9612353 https://doaj.org/article/16188743fcfe49c9835ea9e8bb9f5df1 |
| Volume | 22 |
| WOSCitedRecordID | wos000875132800001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB6xXQ5wWJ4LgaUyCAku0TZxUtsntEUtcGgVrQAVCSnyK0ulKt1tsxz57YxdJ9sKxIWLD_ZEmmjG87DH3wC8ziVTlNo8Rt9g40wYFQtOaczSYao01UOjKt9sgs1mfD4XRThw24SyytYmekNtVtqdkZ-mLOWuiUGevbu8il3XKHe7GlpoHMChQyrLenA4Gs-K8y7lopiBbfGEKCb3pxv0hgMmPB7ljRfyYP1_muQdn7RfL7njgCb3_pf1-3AUQk9yttWVB3DL1g_h7g4g4SP43jZ5IF_9m3Mia0Omvt7SkgDFehGP0PMZ8kEuGlJ4eM6anLd1SPgNhsFkspQNmcjlkhRrdxXkFh7Dl8n48_uPcei_EGsM25pYVMxIzlWFKYlJjJap5pj9pFpUUiaMCZQlYzpTJseo1w5zlbChzdB8ChS0GdBj6NWr2j4FMsiUrbixVguVpQPLRaaooSa1eSIrqiJ428qj1AGc3PXIWJaYpDjRlZ3oInjVkV5uETn-RjRyQu0IHIi2n1itL8qwJ0vXK4BjBFXpCrnWqKO5lcJypUSVmyqJ4I1TidJtdWRGy_BiAX_JgWaVZ8yB06UiERGctJIvgw3YlDdij-Blt4y7113JyNqurj0NhrjuPW8ET7ZK1vGMoQWylCAbbE_99n5qf6Ve_PAI4cKB6uT02b_Zeg53UveYw9XniBPoNetr-wJu65_NYrPuwwGbMz_yfthWfX9igeP01xjnik_T4ttviVEwWw |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VFAk48C4sFDAIBJdVs_ZubB8QaoHQqE20Qi0qEtLWry2Rok1JUhB_it_IeF9NBOLWA9fYicbZzzPj9cz3ATxPFNeMuSTE2ODCWFodSsFYyGmPasNMz-q8FJvgo5E4OpLpGvxqemF8WWXjE0tHbafGvyPfopwKL2KQxG9Ov4VeNcrfrjYSGhUs9tzPH3hkm78evMPn-4LS_vuDt7thrSoQGkxGFqHMuVVC6BwTbRtZo6gRmNNTI3OlIs4lWsi5ibVNMJdzvURHvOdidAoSzbddhr97CdZjBLvowHo6GKaf2yMewxNfxV_EmOxuzTH6drks-S_Po14pDvBnCFiKgav1mUsBr3_jf_urbsL1OrUm29VeuAVrrrgN15YIF-_Al0bEgnwqe-qJKiwZlvWkjtRUsyfhDkZ2Sz6o8YKkJf1oQT42dVb4HUzzSX-iFqSvJhOSzvxVlx-4C4cXsrwN6BTTwt0H0o21y4V1zkgd064TMtbMMktdEqmc6QBeNc8_MzX5utcAmWR4CPNQyVqoBPCsnXpaMY78bdKOB1E7wZOElx9MZydZ7XMyr4UgMEPMTY5WG9yDiVPSCa1lntg8CuClh2DmXRkaY1TdkYFL8qRg2Tb35HtURjKAzQZpWe3j5tk5zAJ42g6jd_JXTqpw07NyDqbwvl85gHsVqFubMXVCkyI0g6_AfWVRqyPF-GvJgC49aVDCHvzbrCdwZfdguJ_tD0Z7D-Eq9Y0rvhZJbkJnMTtzj-Cy-b4Yz2eP621M4Piit8NvPymIWw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFiE48C4YCiwIBBcr8a6d9R4QaimGqDSyEEVFQnL3WSJFTklSEH-NX8esX00E4tYDV-_amrW_eax35huAp4nkijGbhOgbbBgLo0KRMhZyOqBKMz0wylXNJvholB4einwNfrW1MD6tsrWJlaE2U-3_kfcop6lvYpDEPdekReS72auTb6HvIOVPWtt2GjVE9uzPH7h9m78c7uK3fkZp9ubj63dh02Eg1BiYLELhuJFpqhwG3SYyWlKdYnxPtXBSRpwLlJZzHSuTYFxnB4mK-MDGaCAELsX0GT73AmxgSB6jjm3kw_38c7fdY7j7q7mMGBP93hw9cZ-LigvzzANWjQL-dAdL_nA1V3PJ-WXX_ufXdh2uNiE32a515Aas2fImXFkiYrwFX9rmFuRTVWtPZGnIfpVnaklDQXsc7qDHN-StHC9IXtGSluRDm3-F92D4T7KJXJBMTiYkn_kjMD9wGw7OZXmbsF5OS3sXSD9W1qXGWi1UTPs2FbFihhlqk0g6pgJ40WKh0A0pu-8NMilwc-ZhU3SwCeBJN_WkZiL526QdD6hugicPry5MZ8dFY4sK3yMhRcQ67VBqjbqZWClsqpRwiXFRAM89HAtv4lAYLZtKDVySJwsrtrkn5aMiEgFstagrGts3L84gF8Djbhitlj-KkqWdnlZzMLT3dcwB3KkB3smMIRWKFKEYfAX6K4taHSnHXytmdOHJhBJ2799iPYJLqAPF--Fo7z5cpr6exacoiS1YX8xO7QO4qL8vxvPZw0ajCRydtzb8BnNVkRs |
| 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=Computer+Vision+and+Machine+Learning-Based+Gait+Pattern+Recognition+for+Flat+Fall+Prediction&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Chen%2C+Biao&rft.au=Chen%2C+Chaoyang&rft.au=Hu%2C+Jie&rft.au=Sayeed%2C+Zain&rft.date=2022-10-19&rft.pub=MDPI+AG&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=22&rft.issue=20&rft_id=info:doi/10.3390%2Fs22207960&rft.externalDocID=A746532919 |
| 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 |