{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T02:15:57Z","timestamp":1773195357133,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011688","name":"Electronic Components and Systems for European Leadership","doi-asserted-by":"publisher","award":["737487"],"award-info":[{"award-number":["737487"]}],"id":[{"id":"10.13039\/501100011688","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson\u2019s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and\/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.<\/jats:p>","DOI":"10.3390\/s21072259","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T21:36:51Z","timestamp":1616621811000},"page":"2259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3931-5141","authenticated-orcid":false,"given":"Silje Ekroll","family":"Jahren","sequence":"first","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}]},{"given":"Niels","family":"Aakvaag","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4628-3280","authenticated-orcid":false,"given":"Frode","family":"Strisland","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"},{"name":"Department of Physics, University of Oslo, 0371 Oslo, Norway"}]},{"given":"Andreas","family":"Vogl","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}]},{"given":"Alessandro","family":"Liberale","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9022-4272","authenticated-orcid":false,"given":"Anders E.","family":"Liverud","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.3390\/s120202255","article-title":"Gait Analysis Using Wearable Sensors","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.1038\/s41598-018-22676-0","article-title":"Profiling walking dysfunction in multiple sclerosis: Characterisation, classification and progression over time","volume":"8","author":"Filli","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1177\/1352458517690823","article-title":"Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis","volume":"23","author":"Motl","year":"2017","journal-title":"Mult. Scler. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tunca, C., Pehlivan, N., Ak, N., Arnrich, B., Salur, G., and Ersoy, C. (2017). Inertial sensor-based robust gait analysis in non-hospital settings for neurological disorders. Sensors, 17.","DOI":"10.3390\/s17040825"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1109\/JSEN.2016.2609392","article-title":"Wearable Inertial Sensors for Human Motion Analysis: A review","volume":"16","author":"Angelica","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbiomech.2019.03.008","article-title":"Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait","volume":"87","author":"Choupina","year":"2019","journal-title":"J. Biomech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rehab.2019.07.004","article-title":"Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: A systematic review and meta-analysis","volume":"63","author":"Quijoux","year":"2020","journal-title":"Ann. Phys. Rehabil. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1109\/TNSRE.2015.2457511","article-title":"A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters","volume":"24","author":"Ferrari","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Teufl, W., Lorenz, M., Miezal, M., Taetz, B., Fr\u00f6hlich, M., and Bleser, G. (2018). Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters. Sensors, 19.","DOI":"10.3390\/s19010038"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.jbiomech.2010.07.003","article-title":"3D gait assessment in young and elderly subjects using foot-worn inertial sensors","volume":"43","author":"Mariani","year":"2010","journal-title":"J. Biomech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1682\/JRRD.2015.04.0065","article-title":"Reliability and validity of the inertial sensor-based Timed \u201cUp and Go\u201d test in individuals affected by stroke","volume":"53","author":"Aminian","year":"2016","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TNSRE.2019.2930751","article-title":"Validity and Reproducibility of Inertial Physilog Sensors for Spatiotemporal Gait Analysis in Patients with Stroke","volume":"27","author":"Lefeber","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zizzo, G., and Ren, L. (2017). Position tracking during human walking using an integrated wearable sensing system. Sensors, 17.","DOI":"10.3390\/s17122866"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TNSRE.2015.2409123","article-title":"Assessment of Foot Trajectory for Human Gait Phase Detection Using Wireless Ultrasonic Sensor Network","volume":"24","author":"Qi","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ashhar, K., Soh, C.B., and Kong, K.H. (2017, January 11\u201315). A wearable ultrasonic sensor network for analysis of bilateral gait symmetry. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037845"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/TNSRE.2014.2357686","article-title":"Ambulatory estimation of relative foot positions by fusing ultrasound and inertial sensor data","volume":"23","author":"Weenk","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7606","DOI":"10.3390\/s110807606","article-title":"Indoor pedestrian navigation using foot-mounted IMU and portable ultrasound range sensors","volume":"11","author":"Girard","year":"2011","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fischer, C., Muthukrishnan, K., Hazas, M., and Gellersen, H. (2008). Ultrasound-Aided Pedestrian Dead Reckoning for Indoor Navigation. ACM, 31\u201336.","DOI":"10.1145\/1410012.1410020"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1109\/COMST.2019.2897800","article-title":"Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives","volume":"21","author":"Sutton","year":"2019","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qi, J., and Liu, G.-P. (2017). A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network. Sensors, 17.","DOI":"10.3390\/s17112554"},{"key":"ref_21","unstructured":"Schwameder, H., Andress, M., Graf, E., and Strutzenberger, G. (July, January 29). Validation of an IMU-system (Gait-Up) to identify gait parameters in normal and induced limping walking conditions. Proceedings of the 33rd International Conference on Biomechanics in Sports, Poitiers, France."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:05Z","timestamp":1760161205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":21,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072259"],"URL":"https:\/\/doi.org\/10.3390\/s21072259","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}