{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:06:36Z","timestamp":1760058396197,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS091056"],"award-info":[{"award-number":["R01NS091056"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inertial measurement units (IMUs) are used to analyze running performance. While leveraging one sensor to estimate kinematic and kinetic variables is common, sparsity limits the number of digital biomarkers that can be evaluated. Shallow recurrent decoder networks (SHRED) can reconstruct a dense set of time-series signals from a single input sensor and have been successful in human mobility applications, highlighting the potential for this algorithm to monitor running. We trained and tested subject-specific SHRED models of nine subjects running on a treadmill to map from one input sensor to the remaining three IMUs. We varied the type of input to reflect experimental parameters that are important in running studies\u2014sensor location, sensor type, sampling rate, and running speed\u2014and compared the error of inferred signals from each input type. Sensor location and type did not impact SHRED inference accuracy, while decreasing the sampling rate affected the accuracy of ankle measurements. All ankle acceleration inferences from these models remained below the minimal detectable change threshold of 12.0 m\/s2. SHRED models trained and tested at multiple speeds did not accurately infer IMU measurements below this threshold. SHRED may broaden the scope of motion analysis by expanding datasets with fewer sensors.<\/jats:p>","DOI":"10.3390\/s25072105","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:35:28Z","timestamp":1743132928000},"page":"2105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluating Sparse Inertial Measurement Unit Configurations for Inferring Treadmill Running Motion"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0329-4119","authenticated-orcid":false,"given":"Mackenzie N.","family":"Pitts","sequence":"first","affiliation":[{"name":"Mechanical Engineering, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9227-2203","authenticated-orcid":false,"given":"Megan R.","family":"Ebers","sequence":"additional","affiliation":[{"name":"Applied Mathematics, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4861-6085","authenticated-orcid":false,"given":"Cristine E.","family":"Agresta","sequence":"additional","affiliation":[{"name":"Rehabilitation Medicine, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4128-9387","authenticated-orcid":false,"given":"Katherine M.","family":"Steele","sequence":"additional","affiliation":[{"name":"Mechanical Engineering, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s40279-022-01760-6","article-title":"Wearables for Running Gait Analysis: A Systematic Review","volume":"53","author":"Mason","year":"2023","journal-title":"Sports Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Preatoni, E., Bergamini, E., Fantozzi, S., Giraud, L.I., Orejel Bustos, A.S., Vannozzi, G., and Camomilla, V. (2022). The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review. Sensors, 22.","DOI":"10.3390\/s22093225"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Benson, L.C., R\u00e4is\u00e4nen, A.M., Clermont, C.A., and Ferber, R. (2022). Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis. Sensors, 22.","DOI":"10.3390\/s22051722"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ptsp.2023.12.010","article-title":"Using Wearable Technology Data to Explain Recreational Running Injury: A Prospective Longitudinal Feasibility Study","volume":"65","author":"Neal","year":"2024","journal-title":"Phys. Ther. Sport"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e17896","DOI":"10.7717\/peerj.17896","article-title":"Consumer-Priced Wearable Sensors Combined with Deep Learning Can Be Used to Accurately Predict Ground Reaction Forces during Various Treadmill Running Conditions","volume":"12","author":"Carter","year":"2024","journal-title":"PeerJ"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/02640414.2022.2107816","article-title":"Tibial Bone Forces Can Be Monitored Using Shoe-Worn Wearable Sensors during Running","volume":"40","author":"Elstub","year":"2022","journal-title":"J. Sports Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TBME.2024.3465373","article-title":"Estimating Ground Reaction Forces from Inertial Sensors","volume":"72","author":"Song","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/JBHI.2020.3014963","article-title":"Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement","volume":"25","author":"Tan","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dorschky, E., Nitschke, M., Mayer, M., Weygers, I., Gassner, H., Seel, T., Eskofier, B.M., and Koelewijn, A.D. (2025). Comparing Sparse Inertial Sensor Setups for Sagittal-Plane Walking and Running Reconstructions. Front. Bioeng. Biotechnol., 13.","DOI":"10.3389\/fbioe.2025.1507162"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wolski, L., Halaki, M., Hiller, C.E., Pappas, E., and Fong Yan, A. (2024). Validity of an Inertial Measurement Unit System to Measure Lower Limb Kinematics at Point of Contact during Incremental High-Speed Running. Sensors, 24.","DOI":"10.3390\/s24175718"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.7736\/KSPE.2018.35.12.1199","article-title":"IMU-Based Joint Angle Estimation Under Various Walking and Running Conditions","volume":"35","author":"Jeon","year":"2018","journal-title":"J. Korean Soc. Precis. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Baker, L.M., Yawar, A., Lieberman, D.E., and Walsh, C.J. (2024). Predicting Overstriding with Wearable IMUs during Treadmill and Overground Running. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-56888-4"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Faisal, A.I., Majumder, S., Mondal, T., Cowan, D., Naseh, S., and Deen, M.J. (2019). Monitoring Methods of Human Body Joints: State-of-the-Art and Research Challenges. Sensors, 19.","DOI":"10.3390\/s19112629"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dorschky, E., Nitschke, M., Martindale, C.F., Van Den Bogert, A.J., Koelewijn, A.D., and Eskofier, B.M. (2020). CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00604"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wouda, F.J., Giuberti, M., Bellusci, G., Maartens, E., Reenalda, J., Van Beijnum, B.-J.F., and Veltink, P.H. (2018). Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors. Front. Physiol., 9.","DOI":"10.3389\/fphys.2018.00218"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.clinbiomech.2018.07.007","article-title":"Accelerations from Wearable Accelerometers Reflect Knee Loading during Running after Anterior Cruciate Ligament Reconstruction","volume":"58","author":"Havens","year":"2018","journal-title":"Clin. Biomech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1111\/cgf.13131","article-title":"Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs","volume":"36","author":"Rosenhahn","year":"2017","journal-title":"Comput. Graph. Forum"},{"key":"ref_18","first-page":"1","article-title":"Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time","volume":"37","author":"Huang","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhou, Y., Habermann, M., Shimada, S., Golyanik, V., Theobalt, C., and Xu, F. (2022, January 19\u201324). Physical Inertial Poser (PIP): Physics-Aware Real-Time Human Motion Tracking from Sparse Inertial Sensors. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01282"},{"key":"ref_20","unstructured":"Wouwe, T.V., Lee, S., Falisse, A., Delp, S., and Liu, C.K. (2024, January 17\u201321). DiffusionPoser: Real-Time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, Seattle, WA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20240054","DOI":"10.1098\/rspa.2024.0054","article-title":"Sensing with Shallow Recurrent Decoder Networks","volume":"480","author":"Williams","year":"2024","journal-title":"Proc. R. Soc. A"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97428","DOI":"10.1109\/ACCESS.2024.3423679","article-title":"Leveraging Arbitrary Mobile Sensor Trajectories with Shallow Recurrent Decoder Networks for Full-State Reconstruction","volume":"12","author":"Ebers","year":"2024","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ebers, M.R., Pitts, M., Steele, K.M., and Kutz, J.N. (2024). Human Motion Data Expansion from Arbitrary Sparse Sensors with Shallow Recurrent Decoders. bioRxiv.","DOI":"10.1101\/2024.06.01.596487"},{"key":"ref_24","unstructured":"Riva, S., Introini, C., Cammi, A., and Kutz, J.N. (2024). Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ingraham, K.A., Ferris, D.P., and Remy, C.D. (2017, January 14\u201316). Using Portable Physiological Sensors to Estimate Energy Cost for \u2018Body-in-the-Loop\u2019 Optimization of Assistive Robotic Devices. Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada.","DOI":"10.1109\/GlobalSIP.2017.8308675"},{"key":"ref_26","first-page":"698","article-title":"Assessing Stride Variables and Vertical Stiffness with GPS-Embedded Accelerometers: Preliminary Insights for the Monitoring of Neuromuscular Fatigue on the Field","volume":"14","author":"Buchheit","year":"2015","journal-title":"J. Sports Sci. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lawson, M., Naemi, R., Needham, R.A., and Chockalingam, N. (2024). Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-Based IMU Acceleration? A Novel Method of Conducting Biomechanical Analysis in the Field Using Artificial Neural Networks. Appl. Sci., 14.","DOI":"10.3390\/app14051730"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/JSEN.2015.2477497","article-title":"A Model for Comparing Over-Ground Running Speed and Accelerometer Derived Step Rate in Elite Level Athletes","volume":"16","author":"Neville","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.jelekin.2019.01.001","article-title":"The Value of Tibial Mounted Inertial Measurement Units to Quantify Running Kinetics in Elite Football (Soccer) Players. A Reliability and Agreement Study Using a Research Orientated and a Clinically Orientated System","volume":"44","author":"Hughes","year":"2019","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s12984-022-00992-x","article-title":"Assessing Inertial Measurement Unit Locations for Freezing of Gait Detection and Patient Preference","volume":"19","author":"Lee","year":"2022","journal-title":"J NeuroEngineering Rehabil."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.gaitpost.2018.09.017","article-title":"The Measurement of Tibial Acceleration in Runners\u2014A Review of the Factors That Can Affect Tibial Acceleration during Running and Evidence-Based Guidelines for Its Use","volume":"67","author":"Sheerin","year":"2019","journal-title":"Gait Posture"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1109\/JBHI.2024.3368042","article-title":"Seven Things to Know about Exercise Classification with Inertial Sensing Wearables","volume":"28","author":"Phan","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yoshida, T., Nozaki, J., Urano, K., Hiroi, K., Kaji, K., Yonezawa, T., and Kawaguchi, N. (2019, January 9\u201313). Sampling Rate Dependency in Pedestrian Walking Speed Estimation Using DualCNN-LSTM. Proceedings of the Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, London, UK.","DOI":"10.1145\/3341162.3343765"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine Learning in Human Movement Biomechanics: Best Practices, Common Pitfalls, and New Opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J. Biomech."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Clouthier, A.L., Ross, G.B., and Graham, R.B. (2020). Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks. Front. Bioeng. Biotechnol., 7.","DOI":"10.3389\/fbioe.2019.00473"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-022-01001-x","article-title":"OpenSense: An Open-Source Toolbox for Inertial-Measurement-Unit-Based Measurement of Lower Extremity Kinematics over Long Durations","volume":"19","author":"Ibarra","year":"2022","journal-title":"J. Neuroeng. Rehabil."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/7\/2105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:03:18Z","timestamp":1760029398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/7\/2105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":36,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["s25072105"],"URL":"https:\/\/doi.org\/10.3390\/s25072105","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,3,27]]}}}