{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:59:21Z","timestamp":1772823561757,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21248389","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"8389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Recognizing Full-Body Exercise Execution Errors Using the Teslasuit"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3252-4533","authenticated-orcid":false,"given":"Polona","family":"Caserman","sequence":"first","affiliation":[{"name":"Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens","family":"Krug","sequence":"additional","affiliation":[{"name":"Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-8744","authenticated-orcid":false,"given":"Stefan","family":"G\u00f6bel","sequence":"additional","affiliation":[{"name":"Research Group Serious Games, Technical University of Darmstadt, Rundeturmstrasse 10, 64283 Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021, October 19). Physical Activity. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/physical-activity."},{"key":"ref_2","unstructured":"World Health Organization (2021, October 19). WHO Guidelines on Physical Activity and Sedentary Behaviour. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240015128."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1001\/jama.2018.14854","article-title":"The Physical Activity Guidelines for Americans","volume":"320","author":"Piercy","year":"2018","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1016\/S2214-109X(18)30357-7","article-title":"Worldwide Trends in Insufficient Physical Activity From 2001 to 2016: A Pooled Analysis of 358 Population-Based Surveys With 1\u00b7 9 Million Participants","volume":"6","author":"Guthold","year":"2018","journal-title":"Lancet Glob. Health"},{"key":"ref_5","unstructured":"Statista (2021, October 19). Wichtigste Trends im Personal Training in Deutschland 2015. Available online: https:\/\/de.statista.com\/statistik\/daten\/studie\/664709\/umfrage\/trends-im-personal-training-in-deutschland\/."},{"key":"ref_6","unstructured":"World Health Organization (2021, October 19). Rehabilitation. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/rehabilitation."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","article-title":"A Survey on Activity Detection and Classification using Wearable Sensors","volume":"17","author":"Cornacchia","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A Survey on Human Activity Recognition using Wearable Sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/JSEN.2014.2370945","article-title":"Wearable Sensors for Human Activity Monitoring: A Review","volume":"15","author":"Mukhopadhyay","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-Based and Vision-Based Human Activity Recognition: A Comprehensive Survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Martin-Niedecken, A.L., Rogers, K., Turmo Vidal, L., Mekler, E.D., and M\u00e1rquez Segura, E. (2019, January 4\u20139). ExerCube vs. Personal Trainer: Evaluating a Holistic, Immersive, and Adaptive Fitness Game Setup. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, Scotland.","DOI":"10.1145\/3290605.3300318"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Born, F., Abramowski, S., and Masuch, M. (2019, January 4\u20136). Exergaming in VR: The Impact of Immersive Embodiment on Motivation, Performance, and Perceived Exertion. Proceedings of the 11th International Conference on Virtual Worlds and Games for Serious Applications, Vienna, Austria.","DOI":"10.1109\/VS-Games.2019.8864579"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1162\/pres_a_00036","article-title":"Astrojumper: Motivating Exercise with an Immersive Virtual Reality Exergame","volume":"20","author":"Finkelstein","year":"2011","journal-title":"Presence Teleoperators Virtual Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/TLT.2010.27","article-title":"A Virtual Reality Dance Training System using Motion Capture Technology","volume":"4","author":"Chan","year":"2011","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Palkowski, A., Redlarski, G., Rzyman, G., and Krawczuk, M. (2018, January 9\u201312). Basic Evaluation of Limb Exercises Based on Electromyography and Classification Methods. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388382"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2017.2736559","article-title":"Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat","volume":"5","author":"Kianifar","year":"2017","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"O\u2019Reilly, M., Whelan, D., Chanialidis, C., Friel, N., Delahunt, E., Ward, T., and Caulfield, B. (2015, January 9\u201312). Evaluating Squat Performance with a Single Inertial Measurement Unit. Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299380"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.1109\/JBHI.2019.2963365","article-title":"Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy","volume":"24","author":"Cai","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"M\u00fcller, P.N., Rauterberg, F., Achenbach, P., Tregel, T., and G\u00f6bel, S. (2021). Physical Exercise Quality Assessment Using Wearable Sensors. Joint Conference on Serious Games, Springer International Publishing.","DOI":"10.1007\/978-3-030-88272-3_17"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., and Fuks, H. (2013, January 7\u20138). Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of the 4th Augmented Human International Conference, Stuttgart, Germany.","DOI":"10.1145\/2459236.2459256"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Caserman, P., Liu, S., and G\u00f6bel, S. (2021). Full-Body Motion Recognition in Immersive Virtual Reality-based Exergame. IEEE Trans. Games, 1\u201310.","DOI":"10.1109\/TVCG.2019.2912607"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"de Villa, S.G., Mart\u00edn, A.J., and Dom\u00ednguez, J.J.G. (2019, January 26\u201328). Implementation of a Lower-Limb Model for Monitoring Exercises in Rehabilitation. Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications, Istanbul, Turkey.","DOI":"10.1109\/MeMeA.2019.8802221"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3389\/fnbot.2018.00024","article-title":"Assisting Movement Training and Execution With Visual and Haptic Feedback","volume":"12","author":"Ewerton","year":"2018","journal-title":"Front. Neurorobot."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Maurer, U., Smailagic, A., Siewiorek, D., and Deisher, M. (2006, January 3\u20135). Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA.","DOI":"10.21236\/ADA534437"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s10514-017-9648-7","article-title":"Using Probabilistic Movement Primitives in Robotics","volume":"42","author":"Paraschos","year":"2018","journal-title":"Auton. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity Recognition Using Cell Phone Accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"sigKDD Explor. Newsl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/10.554760","article-title":"A Triaxial Accelerometer and Portable Data Processing Unit for the Assessment of Daily Physical Activity","volume":"44","author":"Bouten","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","first-page":"396","article-title":"Functional Movement Screening: The Use of Fundamental Movements as an Assessment of Function-Part 1","volume":"9","author":"Cook","year":"2014","journal-title":"Int. J. Sports Phys. Ther."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., and Weiss, G.M. (2014, January 17). Limitations with Activity Recognition Methodology & Data Sets. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2641306"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., and Weiss, G.M. (2014, January 24\u201326). The Benefits of Personalized Smartphone-Based Activity Recognition Models. Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, PA, USA.","DOI":"10.1137\/1.9781611973440.71"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1590\/S1980-65742016000100005","article-title":"Longer Repetition Duration Increases Muscle Activation and Blood Lactate Response in Matched Resistance Training Protocols","volume":"22","author":"Diniz","year":"2016","journal-title":"Motriz Revista de Educa\u00e7\u00e3o F\u00edsica"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1111\/sms.12678","article-title":"Effects of Velocity Loss During Resistance Training on Athletic Performance, Strength Gains and Muscle Adaptations","volume":"27","author":"Dorado","year":"2017","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s40279-015-0304-0","article-title":"Effect of Repetition Duration During Resistance Training on Muscle Hypertrophy: A Systematic Review and Meta-Analysis","volume":"45","author":"Schoenfeld","year":"2015","journal-title":"Sports Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1519\/JSC.0000000000001044","article-title":"Variations in Repetition Duration and Repetition Numbers Influence Muscular Activation and Blood Lactate Response in Protocols Equalized by Time Under Tension","volume":"30","author":"Lacerda","year":"2016","journal-title":"J. Strength Cond. Res."},{"key":"ref_35","unstructured":"Gavrila, D.M., and Davis, L.S. (1995, January 26\u201328). Towards 3-D Model-Based Tracking and Recognition of Human Movement: A Multi-View Approach. Proceedings of the International Workshop on Automatic Face-and Gesture-Recognition, Zurich, Switzerland."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8389\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:49:05Z","timestamp":1760168945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248389"],"URL":"https:\/\/doi.org\/10.3390\/s21248389","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}