{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:31:16Z","timestamp":1753893076400,"version":"3.41.2"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Robotic lower-limb prostheses, with their actively powered joints, may significantly improve amputee users\u2019 mobility and enable them to obtain healthy-like gait in various modes of locomotion in daily life. However, timely recognition of the amputee users\u2019 locomotive mode and mode transition still remains a major challenge in robotic lower-limb prosthesis control. In the paper, the authors present a new multi-dimensional dynamic time warping (mDTW)-based intent recognizer to provide high-accuracy recognition of the locomotion mode\/mode transition sufficiently early in the swing phase, such that the prosthesis\u2019 joint-level motion controller can operate in the correct locomotive mode and assist the user to complete the desired (and often power-demanding) motion in the stance phase. To support the intent recognizer development, the authors conducted a multi-modal gait data collection study to obtain the related sensor signal data in various modes of locomotion. The collected data were then segmented into individual cycles, generating the templates used in the mDTW classifier. Considering the large number of sensor signals available, we conducted feature selection to identify the most useful sensor signals as the input to the mDTW classifier. We also augmented the standard mDTW algorithm with a voting mechanism to make full use of the data generated from the multiple subjects. To validate the proposed intent recognizer, we characterized its performance using the data cumulated at different percentages of progression into the gait cycle (starting from the beginning of the swing phase). It was shown that the mDTW classifier was able to recognize three locomotive mode\/mode transitions (walking, walking to stair climbing, and walking to stair descending) with 99.08% accuracy at 30% progression into the gait cycle, well before the stance phase starts. With its high performance, low computational load, and easy personalization (through individual template generation), the proposed mDTW intent recognizer may become a highly useful building block of a prosthesis control system to facilitate the robotic prostheses\u2019 real-world use among lower-limb amputees.<\/jats:p>","DOI":"10.3389\/frobt.2024.1267072","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T04:18:10Z","timestamp":1712895490000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control"],"prefix":"10.3389","volume":"11","author":[{"given":"Md Rejwanul","family":"Haque","sequence":"first","affiliation":[]},{"given":"Md Rafi","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Edward","family":"Sazonov","sequence":"additional","affiliation":[]},{"given":"Xiangrong","family":"Shen","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1038\/s41551-020-00619-3","article-title":"Design and clinical implementation of an open-source bionic leg","volume":"4","author":"Azocar","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"B2","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1080\/03091900701404043","article-title":"Level walking and stair climbing gait in above-knee amputees","volume":"33","author":"Bae","year":"2009","journal-title":"J. Med. Eng. Technol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"5393","DOI":"10.1109\/lra.2020.3007480","article-title":"Machine learning model comparisons of user independent and dependent intent recognition systems for powered prostheses","volume":"5","author":"Bhakta","year":"2020","journal-title":"IEEE Robotics Automation Lett."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.1109\/tnsre.2021.3107780","article-title":"Real-time activity recognition with instantaneous characteristic features of thigh kinematics","volume":"29","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabilitation Eng."},{"key":"B5","doi-asserted-by":"publisher","first-page":"S38","DOI":"10.1016\/j.gaitpost.2007.12.068","article-title":"The effects of the \u2018Power Knee\u2019 prosthesis on amputees metabolic cost of walking and symmetry of gait\u2014preliminary results","volume":"28","author":"Cutti","year":"2008","journal-title":"Gait Posture"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1109\/SoutheastCon42311.2019.9020642","article-title":"Design and preliminary testing of an instrumented exoskeleton for walking gait measurement","author":"Haque","year":"2019","journal-title":"IEEE SoutheastCon"},{"key":"B7","doi-asserted-by":"publisher","first-page":"781","DOI":"10.3390\/s21030781","article-title":"A lightweight exoskeleton-based portable gait data collection system","volume":"21","author":"Haque","year":"2021","journal-title":"Sensors"},{"key":"B8","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.gaitpost.2006.04.013","article-title":"Control of lateral balance in walking. Experimental findings in normal subjects and above-knee amputees","volume":"25","author":"Hof","year":"2007","journal-title":"Gait Posture"},{"key":"B9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TBME.2008.2003293","article-title":"A strategy for identifying locomotion modes using surface electromyography","volume":"56","author":"Huang","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B10","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1109\/TBME.2011.2161671","article-title":"Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion","volume":"58","author":"Huang","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B11","first-page":"141","article-title":"Automated dance motion evaluation using dynamic time warping and Laban movement analysis","author":"Jang","year":"2017"},{"key":"B12","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1097\/01.phm.0000174665.74933.0b","article-title":"A clinical comparison of variable-damping and mechanically passive prosthetic knee devices","volume":"84","author":"Johansson","year":"2005","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"B13","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/mra.2014.2360303","article-title":"A robotic leg prosthesis: design, control, and implementation","volume":"21","author":"Lawson","year":"2014","journal-title":"IEEE Robotics Automation Mag."},{"key":"B14","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TNSRE.2012.2225640","article-title":"Control of stair ascent and descent with a powered transfemoral prosthesis","volume":"21","author":"Lawson","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabilitation Eng."},{"key":"B15","article-title":"The symmetric time-warp problem: from continuous to discrete","author":"Liberman","year":"1983","journal-title":"Time Warps, String Ed. Macromol. Theory Pract. Sequence Comp"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/tpami.2005.249","article-title":"Theoretical bounds of majority voting performance for a binary classification problem","volume":"27","author":"Narasimhamurthy","year":"2005","journal-title":"IEEE Trans. Pattern Analysis Mach. Intell."},{"key":"B17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1097\/jpo.0b013e318195b917","article-title":"Functional stability of transfemoral amputee gait using the 3R80 and total knee 2000 prosthetic knee units","volume":"21","author":"Silver-Thorn","year":"2009","journal-title":"J. Prosthetics Orthot."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1109\/tnsre.2019.2909585","article-title":"A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis","volume":"27","author":"Su","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabilitation Eng."},{"key":"B19","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1177\/0278364907084588","article-title":"Design and control of a powered transfemoral prosthesis","volume":"27","author":"Sup","year":"2008","journal-title":"Int. J. robotics Res."},{"key":"B20","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1109\/TBME.2009.2034734","article-title":"Multiclass real-time intent recognition of a powered lower limb prosthesis","volume":"57","author":"Varol","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B21","doi-asserted-by":"publisher","first-page":"42","DOI":"10.2106\/00004623-197658010-00007","article-title":"Energy cost of walking of amputees: the influence of level of amputation","volume":"58","author":"Waters","year":"1976","journal-title":"J. Bone Jt. Surg. Am."},{"key":"B22","first-page":"4653","article-title":"Time series data augmentation for deep learning: a survey","author":"Wen","year":"2021"},{"key":"B23","doi-asserted-by":"publisher","first-page":"20002","DOI":"10.3934\/mbe.2023886","article-title":"A gait stability evaluation method based on wearable acceleration sensors","volume":"20","author":"Weng","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"B24","doi-asserted-by":"crossref","DOI":"10.1002\/9780470549148","volume-title":"Biomechanics and motor control of human movement","author":"Winter","year":"2009"},{"key":"B25","first-page":"1587","article-title":"An intent recognition strategy for transfemoral amputee ambulation across different locomotion modes","author":"Young","year":""},{"key":"B26","first-page":"311","article-title":"Classifying the intent of novel users during human locomotion using powered lower limb prostheses","author":"Young","year":""},{"key":"B27","doi-asserted-by":"publisher","first-page":"7005","DOI":"10.1109\/jsen.2022.3146446","article-title":"Lower limb motion intention recognition based on sEMG fusion features","volume":"22","author":"Zhang","year":"2022","journal-title":"IEEE Sensors J."},{"key":"B28","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s12984-015-0011-y","article-title":"A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition","volume":"12","author":"Zhang","year":"2015","journal-title":"J. neuroengineering rehabilitation"},{"key":"B29","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.apmr.2007.11.005","article-title":"Estimating the prevalence of limb loss in the United States: 2005 to 2050","volume":"89","author":"Ziegler-Graham","year":"2008","journal-title":"Arch. Phys. Med. Rehabil."}],"container-title":["Frontiers in Robotics and AI"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2024.1267072\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T04:18:13Z","timestamp":1712895493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frobt.2024.1267072\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":29,"alternative-id":["10.3389\/frobt.2024.1267072"],"URL":"https:\/\/doi.org\/10.3389\/frobt.2024.1267072","relation":{},"ISSN":["2296-9144"],"issn-type":[{"type":"electronic","value":"2296-9144"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"article-number":"1267072"}}