Recognizing Full-Body Exercise Execution Errors Using the Teslasuit
Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermor...
Saved in:
Published in | Sensors (Basel, Switzerland) Vol. 21; no. 24; p. 8389 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
15.12.2021
MDPI |
Subjects | |
Online Access | Get full text |
ISSN | 1424-8220 1424-8220 |
DOI | 10.3390/s21248389 |
Cover
Loading…
Abstract | Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements. |
---|---|
AbstractList | Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements. Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements.Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise executions. Existing health or fitness applications often neglect accurate full-body motion recognition and focus on a single body part. Furthermore, they often detect only specific errors or provide feedback first after the execution. This lack raises the necessity for the automated detection of full-body execution errors in real-time to assist users in correcting motor skills. To address this challenge, we propose a method for movement assessment using a full-body haptic motion capture suit. We train probabilistic movement models using the data of 10 inertial sensors to detect exercise execution errors. Additionally, we provide haptic feedback, employing transcutaneous electrical nerve stimulation immediately, as soon as an error occurs, to correct the movements. The results based on a dataset collected from 15 subjects show that our approach can detect severe movement execution errors directly during the workout and provide haptic feedback at respective body locations. These results suggest that a haptic full-body motion capture suit, such as the Teslasuit, is promising for movement assessment and can give appropriate haptic feedback to the users so that they can improve their movements. |
Author | Krug, Clemens Caserman, Polona Göbel, Stefan |
AuthorAffiliation | Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany; clemens.krug@stud.tu-darmstadt.de (C.K.); stefan_peter.goebel@tu-darmstadt.de (S.G.) |
AuthorAffiliation_xml | – name: Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany; clemens.krug@stud.tu-darmstadt.de (C.K.); stefan_peter.goebel@tu-darmstadt.de (S.G.) |
Author_xml | – sequence: 1 givenname: Polona orcidid: 0000-0002-3252-4533 surname: Caserman fullname: Caserman, Polona – sequence: 2 givenname: Clemens surname: Krug fullname: Krug, Clemens – sequence: 3 givenname: Stefan orcidid: 0000-0003-3651-8744 surname: Göbel fullname: Göbel, Stefan |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34960481$$D View this record in MEDLINE/PubMed |
BookMark | eNptkktr3DAQgEVJyas59A8UQy_NwY1etuRLoVk2bSBQKMlZyGNpo0UrpZJdkv76yN10SUJPGjSfPmY0c4T2QgwGofcEf2asw2eZEsolk90bdEg45bWkFO89iw_QUc5rjCljTO6jA8a7FnNJDtHip4G4Cu6PC6vqYvK-Po_DQ7W8NwlcNnMA0-hiqJYpxZSrmzyT462prk32Ok9ufIfeWu2zOXk6j9HNxfJ68b2--vHtcvH1qgbedmOtO8aHzkjbEsm0YbKVfddoBrbnA7aciMEKwTGhvdAWKHQEZNtKaqmAgVF2jC633iHqtbpLbqPTg4raqb8XMa2UTqMDb5TVnBrgZKC45aXVHgSWLWMaACwQKK4vW9fd1G_MACaMSfsX0peZ4G7VKv5WUmDWyK4IPj0JUvw1mTyqjctgvNfBxCkr2pKGECopL-jHV-g6TimUr5opKhuBRVOoD88r2pXyb1YFON0CkGLOydgdQrCa90Dt9qCwZ69YcKOe51iacf4_Lx4BdxqzKg |
CitedBy_id | crossref_primary_10_15701_kcgs_2022_28_3_1 crossref_primary_10_1109_TOH_2023_3269885 crossref_primary_10_1109_ACCESS_2022_3198935 crossref_primary_10_2196_52153 crossref_primary_10_3390_app13010162 crossref_primary_10_2174_0126662558294125240307094426 crossref_primary_10_3390_s23146258 crossref_primary_10_3390_ijerph21010079 crossref_primary_10_3390_s24216946 crossref_primary_10_3390_s24227351 |
Cites_doi | 10.1016/j.patcog.2020.107561 10.1519/JSC.0000000000001044 10.1145/3290605.3300318 10.1109/JSEN.2014.2370945 10.1137/1.9781611973440.71 10.1109/IIPHDW.2018.8388382 10.1590/S1980-65742016000100005 10.3389/fnbot.2018.00024 10.1109/JBHI.2019.2963365 10.1007/978-3-030-88272-3_17 10.1016/S2214-109X(18)30357-7 10.1109/MeMeA.2019.8802221 10.1109/SURV.2012.110112.00192 10.1145/1964897.1964918 10.1109/TLT.2010.27 10.1109/TVCG.2019.2912607 10.1001/jama.2018.14854 10.1145/2638728.2641306 10.1111/sms.12678 10.1109/JSEN.2016.2628346 10.1007/s10514-017-9648-7 10.1007/s40279-015-0304-0 10.1109/10.554760 10.1109/BSN.2015.7299380 10.1109/VS-Games.2019.8864579 10.1145/2459236.2459256 10.1109/JTEHM.2017.2736559 10.21236/ADA534437 10.1162/pres_a_00036 |
ContentType | Journal Article |
Copyright | 2021 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. 2021 by the authors. 2021 |
Copyright_xml | – notice: 2021 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: 2021 by the authors. 2021 |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU COVID DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.3390/s21248389 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic 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 ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
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 Coronavirus Research Database 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 | MEDLINE - Academic Publicly Available Content Database MEDLINE CrossRef |
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: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1424-8220 |
ExternalDocumentID | oai_doaj_org_article_fa42ec41d2064604bc708633acccfc1c PMC8703589 34960481 10_3390_s21248389 |
Genre | Journal Article |
GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALIPV 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 PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. ABJCF ARAPS CGR CUY CVF ECM EIF HCIFZ KB. M7S NPM PDBOC 7XB 8FK AZQEC COVID DWQXO K9. PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM PUEGO |
ID | FETCH-LOGICAL-c469t-a934d9e8f6183ae3868b95a3cfb4d0f417df774012b7afc2c91c86682f27cd323 |
IEDL.DBID | M48 |
ISSN | 1424-8220 |
IngestDate | Wed Aug 27 00:51:51 EDT 2025 Thu Aug 21 13:56:08 EDT 2025 Fri Jul 11 08:43:37 EDT 2025 Fri Jul 25 20:09:12 EDT 2025 Wed Feb 19 02:11:05 EST 2025 Thu Apr 24 23:07:40 EDT 2025 Tue Jul 01 02:41:38 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 24 |
Keywords | haptic feedback imitation learning human exercise assessment transcutaneous electrical nerve stimulation full-body motion capture inertial measurement units |
Language | English |
License | https://creativecommons.org/licenses/by/4.0 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-c469t-a934d9e8f6183ae3868b95a3cfb4d0f417df774012b7afc2c91c86682f27cd323 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0003-3651-8744 0000-0002-3252-4533 |
OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3390/s21248389 |
PMID | 34960481 |
PQID | 2612857075 |
PQPubID | 2032333 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_fa42ec41d2064604bc708633acccfc1c pubmedcentral_primary_oai_pubmedcentral_nih_gov_8703589 proquest_miscellaneous_2615112824 proquest_journals_2612857075 pubmed_primary_34960481 crossref_primary_10_3390_s21248389 crossref_citationtrail_10_3390_s21248389 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 20211215 |
PublicationDateYYYYMMDD | 2021-12-15 |
PublicationDate_xml | – month: 12 year: 2021 text: 20211215 day: 15 |
PublicationDecade | 2020 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland – name: Basel |
PublicationTitle | Sensors (Basel, Switzerland) |
PublicationTitleAlternate | Sensors (Basel) |
PublicationYear | 2021 |
Publisher | MDPI AG MDPI |
Publisher_xml | – name: MDPI AG – name: MDPI |
References | Cornacchia (ref_7) 2017; 17 Lacerda (ref_34) 2016; 30 Chan (ref_14) 2011; 4 Paraschos (ref_25) 2018; 42 Bouten (ref_27) 1997; 44 ref_35 ref_12 ref_11 ref_30 Kianifar (ref_16) 2017; 5 ref_19 Ewerton (ref_23) 2018; 12 ref_17 ref_15 Cook (ref_28) 2014; 9 Mukhopadhyay (ref_9) 2015; 15 Diniz (ref_31) 2016; 22 Lara (ref_8) 2013; 15 Kwapisz (ref_26) 2011; 12 Dorado (ref_32) 2017; 27 ref_24 ref_22 ref_21 ref_20 ref_1 Dang (ref_10) 2020; 108 ref_2 Finkelstein (ref_13) 2011; 20 ref_29 Cai (ref_18) 2020; 24 Piercy (ref_3) 2018; 320 ref_5 Guthold (ref_4) 2018; 6 ref_6 Schoenfeld (ref_33) 2015; 45 |
References_xml | – volume: 108 start-page: 107561 year: 2020 ident: ref_10 article-title: Sensor-Based and Vision-Based Human Activity Recognition: A Comprehensive Survey publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107561 – volume: 30 start-page: 251 year: 2016 ident: ref_34 article-title: Variations in Repetition Duration and Repetition Numbers Influence Muscular Activation and Blood Lactate Response in Protocols Equalized by Time Under Tension publication-title: J. Strength Cond. Res. doi: 10.1519/JSC.0000000000001044 – ident: ref_11 doi: 10.1145/3290605.3300318 – ident: ref_5 – volume: 15 start-page: 1321 year: 2015 ident: ref_9 article-title: Wearable Sensors for Human Activity Monitoring: A Review publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2014.2370945 – ident: ref_30 doi: 10.1137/1.9781611973440.71 – ident: ref_15 doi: 10.1109/IIPHDW.2018.8388382 – volume: 22 start-page: 35 year: 2016 ident: ref_31 article-title: Longer Repetition Duration Increases Muscle Activation and Blood Lactate Response in Matched Resistance Training Protocols publication-title: Motriz Revista de Educação Física doi: 10.1590/S1980-65742016000100005 – volume: 12 start-page: 24 year: 2018 ident: ref_23 article-title: Assisting Movement Training and Execution With Visual and Haptic Feedback publication-title: Front. Neurorobot. doi: 10.3389/fnbot.2018.00024 – ident: ref_1 – volume: 24 start-page: 2630 year: 2020 ident: ref_18 article-title: Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2019.2963365 – ident: ref_19 doi: 10.1007/978-3-030-88272-3_17 – ident: ref_35 – volume: 6 start-page: 1077 year: 2018 ident: ref_4 article-title: Worldwide Trends in Insufficient Physical Activity From 2001 to 2016: A Pooled Analysis of 358 Population-Based Surveys With 1· 9 Million Participants publication-title: Lancet Glob. Health doi: 10.1016/S2214-109X(18)30357-7 – ident: ref_22 doi: 10.1109/MeMeA.2019.8802221 – volume: 15 start-page: 1192 year: 2013 ident: ref_8 article-title: A Survey on Human Activity Recognition using Wearable Sensors publication-title: IEEE Commun. Surv. doi: 10.1109/SURV.2012.110112.00192 – volume: 12 start-page: 74 year: 2011 ident: ref_26 article-title: Activity Recognition Using Cell Phone Accelerometers publication-title: sigKDD Explor. Newsl. doi: 10.1145/1964897.1964918 – ident: ref_6 – ident: ref_2 – volume: 4 start-page: 187 year: 2011 ident: ref_14 article-title: A Virtual Reality Dance Training System using Motion Capture Technology publication-title: IEEE Trans. Learn. Technol. doi: 10.1109/TLT.2010.27 – ident: ref_21 doi: 10.1109/TVCG.2019.2912607 – volume: 320 start-page: 2020 year: 2018 ident: ref_3 article-title: The Physical Activity Guidelines for Americans publication-title: JAMA doi: 10.1001/jama.2018.14854 – ident: ref_29 doi: 10.1145/2638728.2641306 – volume: 27 start-page: 724 year: 2017 ident: ref_32 article-title: Effects of Velocity Loss During Resistance Training on Athletic Performance, Strength Gains and Muscle Adaptations publication-title: Scand. J. Med. Sci. Sports doi: 10.1111/sms.12678 – volume: 17 start-page: 386 year: 2017 ident: ref_7 article-title: A Survey on Activity Detection and Classification using Wearable Sensors publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2016.2628346 – volume: 42 start-page: 529 year: 2018 ident: ref_25 article-title: Using Probabilistic Movement Primitives in Robotics publication-title: Auton. Robot. doi: 10.1007/s10514-017-9648-7 – volume: 45 start-page: 577 year: 2015 ident: ref_33 article-title: Effect of Repetition Duration During Resistance Training on Muscle Hypertrophy: A Systematic Review and Meta-Analysis publication-title: Sports Med. doi: 10.1007/s40279-015-0304-0 – volume: 44 start-page: 136 year: 1997 ident: ref_27 article-title: A Triaxial Accelerometer and Portable Data Processing Unit for the Assessment of Daily Physical Activity publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.554760 – ident: ref_17 doi: 10.1109/BSN.2015.7299380 – ident: ref_12 doi: 10.1109/VS-Games.2019.8864579 – ident: ref_20 doi: 10.1145/2459236.2459256 – volume: 5 start-page: 1 year: 2017 ident: ref_16 article-title: Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat publication-title: IEEE J. Transl. Eng. Health Med. doi: 10.1109/JTEHM.2017.2736559 – ident: ref_24 doi: 10.21236/ADA534437 – volume: 20 start-page: 78 year: 2011 ident: ref_13 article-title: Astrojumper: Motivating Exercise with an Immersive Virtual Reality Exergame publication-title: Presence Teleoperators Virtual Environ. doi: 10.1162/pres_a_00036 – volume: 9 start-page: 396 year: 2014 ident: ref_28 article-title: Functional Movement Screening: The Use of Fundamental Movements as an Assessment of Function-Part 1 publication-title: Int. J. Sports Phys. Ther. |
SSID | ssj0023338 |
Score | 2.4143076 |
Snippet | Regular physical exercise is essential for overall health; however, it is also crucial to mitigate the probability of injuries due to incorrect exercise... |
SourceID | doaj pubmedcentral proquest pubmed crossref |
SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
StartPage | 8389 |
SubjectTerms | Accelerometers COVID-19 Exercise Feedback full-body motion capture haptic feedback Haptics human exercise assessment Humans imitation learning inertial measurement units Machine learning Motion Motion capture Motor Skills Movement Physical fitness Rehabilitation Sensors Training transcutaneous electrical nerve stimulation |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pa90wDBajp-5Q1m5rs7YjGzvsYvpiOYlzbMsrZbAdRgu9BVuxaWHkjffjsP71lZK88F4p7NJbEuvgSLKkD4tPAN9ChZTFWKnCa6c4-uXKVwYVxgnmPnB06Loqf_4qrm_Nj7v8bmPUl_SE9fTAveLOojM6kMkazcmzmBhPJVfhiI6IImUk0Zdz3hpMDVALGXn1PELIoP5swQHaWJRZ7hvZpyPpf6myfN4guZFxrt7B3lAqpuf9FvfhTWgP4O0GgeB7uPzd9_888lsqaFJdzJp_6XSYoyQP1HlWOp3PZ_NF2nUIpFz0pTeBnWGxelh-gNur6c3ltRrGIihiLLtUrkLTVMHGgo-jC2gL66vcIUVvmkk0WdnEUgbtaV-6SJqqjGxRWB11SQ1q_Ag77awNR5BqS7oRijItxFcMHcpoEU1unfPGN5jA97W6aho4w2V0xZ-asYNoth41m8DXUfRvT5TxktCF6HwUEG7r7gNbvB4sXv_P4gmcrC1WDwduUQsTmnD1l3kCX8ZlPipy_-HaMFt1MlJeWm0SOOwNPO5EePOFOieBcsv0W1vdXmkf7js6bo54mNvq02v82zHsammaybTK8hPYWc5X4ZSrnqX_3Dn4E3SzAMY priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB5BucAB8SZQUEAcuFjdeJzYOSFabVUhwQG10t4i27GhUpWUZPcAvx6P4013UcUtiedgzcvzxaNvAD64Gm3hfc0qwzUL2a9kphbI0C-wNC5kh9hV-fVbdXYhvqzKVfrhNqa2ym1OjIm67S39Iz8iqisiY5flp-tfjKZG0e1qGqFxF-4RdRm1dMnVDeDCgL8mNiEM0P5oDGlaKKSJ7jtnUKTqv62-_LdNcufcOX0ED1PBmH-eLPwY7rjuCTzYoRF8Ciffpy6gP-EtJ0zJjvv2d75M05TowUb_ypfD0A9jHvsE8lD65ecuuMS4uVw_g4vT5fnJGUvDEZgNiHbNdI2irZ3yVQhK7VBVytSlRuuNaBdeFLL1ksbtcSO1t9zWhVVVpbjn0rbI8TkcdH3nXkLOleUtEZVxor8KAEJ6hShKpbURpsUMPm7V1djEHE4DLK6agCBIs82s2Qzez6LXE13GbULHpPNZgBiu44d--NGkgGm8FtxZUbQ8FE3VQhgrA_pC1NZabwubweHWYk0Ku7G5cZIM3s3LIWDoFkR3rt9EGSoyFRcZvJgMPO-E2POJQCcDuWf6va3ur3SXPyMpd8h7WKr61f-39Rruc2qKKTgrykM4WA8b9yZUNWvzNrruXydL95I priority: 102 providerName: ProQuest |
Title | Recognizing Full-Body Exercise Execution Errors Using the Teslasuit |
URI | https://www.ncbi.nlm.nih.gov/pubmed/34960481 https://www.proquest.com/docview/2612857075 https://www.proquest.com/docview/2615112824 https://pubmed.ncbi.nlm.nih.gov/PMC8703589 https://doaj.org/article/fa42ec41d2064604bc708633acccfc1c |
Volume | 21 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9wwEB7ygJIeSt91my5u6aEXt7EkW_KhlG7YbSgklJCFvRlJltJAsBPvLjT99Z3xi7jsqRdjW2MQ85Dms4ZvAD64jNvY-yxKDdMRrn5JZDLBI-6PeGIcrg5NVeXpWXqyED-WyXIH-h6bnQJXW6Ed9ZNa1Nefft_efcWA_0KIEyH75xUuv0LhzrsL-7ghSYrPUzEcJjCOMKwlFRqLH8ADoksnxpTRrtSQ92_LOP8tnLy3E80fw6MuhQy_tTZ_AjuufAoP7xELPoPj87Yu6A8-hYQyo2lV3IWzrr8S3djG48JZXVf1KmwqB0JMBsMLh06y2lytn8NiPrs4Pom6dgmRRYy7jnTGRZE55VMMU-24SpXJEs2tN6I48qiYwktqwMeM1N4ym8VWpalinklbcMZfwF5Zle4VhExZVhB1GSNCLIQU0ivORaK0NsIUPICPvbpy23GJU0uL6xwxBSk5H5QcwPtB9KYl0NgmNCWdDwLEed28qOrLvAuh3GvBnBVxwTCNQqsZKxGPca6ttd7GNoDD3mJ570c5MaQRh79MAng3DGMI0bmILl21aWQo7VRMBPCyNfAwk95BApAj04-mOh4pr341NN24EvJEZa__-8s3cMCogiZmUZwcwt663ri3mAKtzQR25VLiVc2_T2B_Ojv7eT5pfidMGtf_C8bCCsY |
linkProvider | Scholars Portal |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB5V5QAcEG9SCgQEEpeoie0kzgEhWrba0scBbaW9BduxoVKVlGRXqPwofiMzeXUXVdx6y2MO1rw8Xzz5BuCtzbiJnMuCRDMVYPaLA50JHnAX8lhbzA5tV-XxSTI9FV_m8XwD_gz_wlBb5ZAT20RdVIa-ke8Q1RWRsafxx4ufAU2NotPVYYRG5xaH9vIXQrbmw8FntO87xvYns71p0E8VCAxCwUWgMi6KzEqXoDcry2UidRYrbpwWRehElBYupTl1TKfKGWayyMgkkcyx1BSciA4w5d_CjTekiErnVwCPI97r2Is4z8KdBrcFITlNkF_Z89rRANfVs_-2Za7sc_v34V5foPqfOo96ABu2fAh3V2gLH8He167r6Dfe-YRhg92quPQn_fQmujCtP_uTuq7qxm_7EnwsNf2ZRRdslmeLx3B6I2p7AptlVdpn4DNpWEHEaIzothCwpE5yLmKplBa64B68H9SVm56pnAZmnOeIWEiz-ahZD96MohcdPcd1Qruk81GAGLXbB1X9Pe8DNHdKMGtEVDAs0pJQaJMi2uNcGWOciYwH24PF8j7Mm_zKKT14Pb7GAKVTF1XaatnKUFErmfDgaWfgcSXE1k-EPR6ka6ZfW-r6m_LsR0sCjnmWxzLb-v-yXsHt6ez4KD86ODl8DncYNeRELIjibdhc1Ev7AiuqhX7ZurEP3246bv4CtqQ0dA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB5VRUJwQLxJKRAQSFys3dhO7BwQou2uWgoVQq20t-A4NlSqkrLZFSo_jV_HTF7dRRW33vKYgzUvzxdPvgF47VJhI-9TluTcMMx-MctTKZjwYxHnDrND01X5-SjZP5EfZ_FsA_70_8JQW2WfE5tEXVSWvpGPiOqKyNhVPPJdW8SXven785-MJkjRSWs_TqN1kUN38QvhW_3uYA9t_Ybz6eR4d591EwaYRVi4YCYVskid9gl6tnFCJzpPYyOsz2Ux9jJShVc0s47nynjLbRpZnSSae65sIYj0ANP_DSXiiGJMzS7BnkDs1zIZCZGORzVuEVILmia_sv81YwKuqm3_bdFc2fOmd-FOV6yGH1rvugcbrrwPt1coDB_A7te2A-k33oWEZ9lOVVyEk26SE13YxrfDyXxezeuw6VEIsewMjx26Y708XTyEk2tR2yPYLKvSPYGQa8sLIknjRL2F4EV5LYSMtTG5zAsRwNteXZntWMtpeMZZhuiFNJsNmg3g1SB63lJ1XCW0QzofBIhdu3lQzb9nXbBm3kjurIwKjgVbMpa5VYj8hDDWWm8jG8B2b7GsC_k6u3TQAF4OrzFY6QTGlK5aNjJU4GouA3jcGnhYCTH3E3lPAGrN9GtLXX9Tnv5oCMEx54pYp1v_X9YLuIkRk306ODp8Crc49eZEnEXxNmwu5kv3DIurRf688eIQvl132PwF58s4qg |
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=Recognizing+Full-Body+Exercise+Execution+Errors+Using+the+Teslasuit&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Caserman%2C+Polona&rft.au=Krug%2C+Clemens&rft.au=G%C3%B6bel%2C+Stefan&rft.date=2021-12-15&rft.pub=MDPI&rft.eissn=1424-8220&rft.volume=21&rft.issue=24&rft_id=info:doi/10.3390%2Fs21248389&rft_id=info%3Apmid%2F34960481&rft.externalDocID=PMC8703589 |
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 |