{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:45:50Z","timestamp":1760240750564,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T00:00:00Z","timestamp":1566864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003958","name":"Stichting voor de Technische Wetenschappen","doi-asserted-by":"publisher","award":["13917"],"award-info":[{"award-number":["13917"]}],"id":[{"id":"10.13039\/501100003958","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Full-body motion capture typically requires sensors\/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors\/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and\/or sufficient computational resources. Therefore, we investigate the following research question: \u201cWhat is the performance of a shallow approach, compared to a deep learning one, for estimating time coherent full-body poses using only five inertial sensors?\u201d. We propose to incorporate past\/future inertial sensor information into a stacked input vector, which is fed to a shallow neural network for estimating full-body poses. Shallow and deep learning approaches are compared using the same input vector configurations. Additionally, the inclusion of acceleration input is evaluated. The results show that a shallow learning approach can estimate full-body poses with a similar accuracy (~6 cm) to that of a deep learning approach (~7 cm). However, the jerk errors are smaller using the deep learning approach, which can be the effect of explicit recurrent modelling. Furthermore, it is shown that the delay using a shallow learning approach (72 ms) is smaller than that of a deep learning approach (117 ms).<\/jats:p>","DOI":"10.3390\/s19173716","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T11:13:30Z","timestamp":1566904410000},"page":"3716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4401-4434","authenticated-orcid":false,"given":"Frank J.","family":"Wouda","sequence":"first","affiliation":[{"name":"Department of Biomedical Signals & Systems, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"given":"Matteo","family":"Giuberti","sequence":"additional","affiliation":[{"name":"RADiCAL Solutions, LLC. 125 West 31st Street, New York, NY 10001, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5040-4714","authenticated-orcid":false,"given":"Nina","family":"Rudigkeit","sequence":"additional","affiliation":[{"name":"Xsens Technologies B.V., Pantheon 6a, 7521 PR Enschede, The Netherlands"}]},{"given":"Bert-Jan F.","family":"van Beijnum","sequence":"additional","affiliation":[{"name":"Department of Biomedical Signals & Systems, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"given":"Mannes","family":"Poel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"given":"Peter H.","family":"Veltink","sequence":"additional","affiliation":[{"name":"Department of Biomedical Signals & Systems, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Adesida, Y., Papi, E., and McGregor, A.H. (2019). Exploring the role of wearable technology in sport kinematics and kinetics: A systematic review. Sensors, 19.","DOI":"10.3390\/s19071597"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.humov.2017.08.005","article-title":"Analysis of gait patterns pre- and post- Single Event Multilevel Surgery in children with Cerebral Palsy by means of Offset-Wise Movement Analysis Profile and Linear Fit Method","volume":"55","author":"Ancillao","year":"2017","journal-title":"Hum. Mov. 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