{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:08:05Z","timestamp":1787029685191,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:00Z","timestamp":1727568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Institute of Science and Technology (KIST) Institutional Program","award":["2E32341"],"award-info":[{"award-number":["2E32341"]}]},{"name":"Korea Institute of Science and Technology (KIST) Institutional Program","award":["2E33141"],"award-info":[{"award-number":["2E33141"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing studies on gait phase estimation generally involve walking experiments using inertial measurement units under limited walking conditions (WCs). In this study, a gait phase estimation algorithm is proposed that uses data from force sensing resistors (FSRs) and a Bi-LSTM model. The proposed algorithm estimates gait phases in real time under various WCs, e.g., walking on paved\/unpaved roads, ascending and descending stairs, and ascending or descending on ramps. The performance of the proposed algorithm is evaluated by performing walking experiments on ten healthy adult participants. An average gait estimation accuracy exceeding 90% is observed with a small error (root mean square error = 0.794, R2 score = 0.906) across various WCs. These results demonstrate the wide applicability of the proposed gait phase estimation algorithm using various insole devices, e.g., in walking aid control, gait disturbance diagnosis in daily life, and motor ability analysis.<\/jats:p>","DOI":"10.3390\/s24196318","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:19:37Z","timestamp":1727680777000},"page":"6318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Continuous Gait Phase Estimation for Multi-Locomotion Tasks Using Ground Reaction Force Data"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1772-5054","authenticated-orcid":false,"given":"Ji Su","family":"Park","sequence":"first","affiliation":[{"name":"Safety Component R&D Center, Gyeonggi Regional Division, Korea Automotive Technology Institute, Siheung-si 15014, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0162-6342","authenticated-orcid":false,"given":"Choong Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref_1","unstructured":"Winter, D.A. (1990). Biomechanics and Motor Control of Human Movement, John Wiley & Sons, Inc.. [2nd ed.]."},{"key":"ref_2","unstructured":"Whittle, M.W. (2014). Gait Analysis: An Introduction, Butterworth-Heinemann."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, C., Kim, Y., Shin, H., and Min, S.D. (2019). Preliminary clinical application of textile insole sensor for hemiparetic gait pattern analysis. Sensors, 19.","DOI":"10.3390\/s19183950"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e22208","DOI":"10.2196\/22208","article-title":"Clinometric gait analysis using smart insoles in patients with hemiplegia after stroke: Pilot study","volume":"8","author":"Seo","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111839","DOI":"10.1016\/j.measurement.2022.111839","article-title":"Wearable gait monitoring for diagnosis of neurodegenerative diseases","volume":"202","author":"Zhao","year":"2022","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1109\/JSEN.2019.2894143","article-title":"Gait phase detection for normal and abnormal gaits using IMU","volume":"19","author":"Han","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, H., Kim, J.W., and Ko, J. (2021). Gait disorder detection and classification method using inertia measurement unit for augmented feedback training in wearable devices. Sensors, 21.","DOI":"10.3390\/s21227676"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1109\/LRA.2021.3068907","article-title":"A phase-shifting based human gait phase estimation for powered transfemoral prostheses","volume":"6","author":"Hong","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9549","DOI":"10.1109\/LRA.2022.3191945","article-title":"A piecewise monotonic gait phase estimation model for controlling a powered transfemoral prosthesis in various locomotion modes","volume":"7","author":"Chen","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3491","DOI":"10.1109\/LRA.2021.3062562","article-title":"Real-time gait phase estimation for robotic hip exoskeleton control during multimodal locomotion","volume":"6","author":"Kang","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TRO.2023.3235584","article-title":"Real-time gait phase and task estimation for controlling a powered ankle exoskeleton on extremely uneven terrain","volume":"39","author":"Medrano","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Seo, K., Park, Y.J., Lee, J., Hyung, S., Lee, M., Kim, J., and Shim, Y. (2019, January 24\u201328). RNN-based on-line continuous gait phase estimation from shank-mounted IMUs to control ankle exoskeletons. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779554"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103842","DOI":"10.1016\/j.robot.2021.103842","article-title":"An adaptive framework of real-time continuous gait phase variable estimation for lower-limb wearable robots","volume":"143","author":"Zhang","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Choi, S., Ko, C., and Kong, K. (2023). Walking-speed-adaptive gait phase estimation for wearable robots. Sensors, 23.","DOI":"10.3390\/s23198276"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Quintero, D., Lambert, D.J., Villarreal, D.J., and Gregg, R.D. (2017, January 27\u201330). Real-time continuous gait phase and speed estimation from a single sensor. Proceedings of the 2017 IEEE Conference on Control Technology and Applications (CCTA), Maui, HI, USA.","DOI":"10.1109\/CCTA.2017.8062565"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TNSRE.2016.2569019","article-title":"A robust parameterization of human gait patterns across phase-shifting perturbations","volume":"25","author":"Villarreal","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/TRO.2021.3122975","article-title":"A novel wavelet-based gait segmentation method for a portable hip exoskeleton","volume":"38","author":"Livolsi","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16952","DOI":"10.1109\/JSEN.2022.3175823","article-title":"Gait pattern identification and phase estimation in continuous multilocomotion mode based on inertial measurement units","volume":"22","author":"Zhang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/TCDS.2019.2933648","article-title":"Locomotion mode identification and gait phase estimation for exoskeletons during continuous multilocomotion tasks","volume":"13","author":"Wu","year":"2019","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_20","unstructured":"Seo, K., Hyung, S., Choi, B.K., Lee, Y., and Shim, Y. (2015, January 26\u201330). A new adaptive frequency oscillator for gait assistance. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Seo, K., Lee, J., Lee, Y., Ha, T., and Shim, Y. (2016, January 16\u201321). Fully autonomous hip exoskeleton saves metabolic cost of walking. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487663"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e11","DOI":"10.1017\/wtc.2022.4","article-title":"Adaptive estimation of continuous gait phase based on capacitive sensors","volume":"3","author":"Xu","year":"2022","journal-title":"Wearable Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1109\/TNSRE.2021.3098689","article-title":"Continuous gait phase estimation using LSTM for robotic transfemoral prosthesis across walking speeds","volume":"29","author":"Lee","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, H., Hu, J., and Wang, Y. (2023, January 26\u201327). Continuous human gait phase estimation considering individual diversity. Proceedings of the 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC58761.2023.00022"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1109\/LRA.2019.2924841","article-title":"Robust and adaptive lower limb prosthesis stance control via extended kalman filter based gait phase estimation","volume":"4","author":"Thatte","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mazon, D.M., Groefsema, M., Schomaker, L.R., and Carloni, R. (2022). IMU-based classification of locomotion modes, transitions, and gait phase with convolutional recurrent neural networks. Sensors, 22.","DOI":"10.3390\/s22228871"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/JBHI.2021.3137413","article-title":"Unsupervised gait phase estimation with domain-adversarial neural network and adaptive window","volume":"26","author":"Choi","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1007\/s10439-023-03151-y","article-title":"Artificial neural network-based activities classification, gait phase estimation, and prediction","volume":"51","author":"Yu","year":"2023","journal-title":"Ann. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TNSRE.2022.3229220","article-title":"Piecewise linear labeling method for speed-adaptability enhancement in human gait phase estimation","volume":"31","author":"Hong","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lu, L., Liu, S., Zhou, Z., Sun, J., Melendez-Calderon, A., and Guo, Z. (2022, January 9\u201311). Continuous gait phase recognition and prediction using lstm network across walking speeds and inclinations. Proceedings of the 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), Guilin, China.","DOI":"10.1109\/ICARM54641.2022.9959678"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Park, J.S., and Kim, C.H. (2022). Ground-reaction-force-based gait analysis and its application to gait disorder assessment: New indices for quantifying walking behavior. Sensors, 22.","DOI":"10.3390\/s22197558"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6516","DOI":"10.1109\/JSEN.2020.2975790","article-title":"Gait phase detection using force sensing resistors","volume":"20","author":"Park","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1080\/01691864.2018.1563500","article-title":"A strain gauge based locomotion mode recognition method using convolutional neural network","volume":"33","author":"Feng","year":"2019","journal-title":"Adv. Robot."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:06:58Z","timestamp":1760112418000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,29]]},"references-count":33,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196318"],"URL":"https:\/\/doi.org\/10.3390\/s24196318","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,29]]}}}