{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T22:24:46Z","timestamp":1774995886226,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,21]],"date-time":"2018-04-21T00:00:00Z","timestamp":1524268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canada Research Chairs Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals\u2019 gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.<\/jats:p>","DOI":"10.3390\/s18041279","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"1279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["A Wearable Gait Phase Detection System Based on Force Myography Techniques"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3219-1871","authenticated-orcid":false,"given":"Xianta","family":"Jiang","sequence":"first","affiliation":[{"name":"MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1574-8770","authenticated-orcid":false,"given":"Kelvin H.T.","family":"Chu","sequence":"additional","affiliation":[{"name":"MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3185-0182","authenticated-orcid":false,"given":"Mahta","family":"Khoshnam","sequence":"additional","affiliation":[{"name":"MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]},{"given":"Carlo","family":"Menon","sequence":"additional","affiliation":[{"name":"MENRVA lab, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.2106\/00004623-199510000-00017","article-title":"Gait Analysis: Principles and Applications","volume":"77","author":"Gage","year":"1995","journal-title":"JBJS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1097\/01241398-199211000-00023","article-title":"Gait analysis: Normal and pathological function","volume":"12","author":"Perry","year":"1992","journal-title":"J. Pediatr. Orthop."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/02640419508732232","article-title":"The use of artificial intelligence in the analysis of sports performance: A review of applications in human gait analysis and future directions for sports biomechanics","volume":"13","author":"Lapham","year":"1995","journal-title":"J. Sports Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Baldwin, R., Bobovych, S., Robucci, R., Patel, C., and Banerjee, N. (2015, January 9\u201313). Gait analysis for fall prediction using hierarchical textile-based capacitive sensor arrays. Proceedings of the 2015 Design, Automation & Test in Europe Conference Exhibition, Grenoble, France.","DOI":"10.7873\/DATE.2015.0943"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.gaitpost.2014.02.001","article-title":"Summary measures for clinical gait analysis: A literature review","volume":"39","author":"Cimolin","year":"2014","journal-title":"Gait Posture"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.knee.2007.04.003","article-title":"Gait analysis of patients following total knee replacement: A systematic review","volume":"14","author":"McClelland","year":"2007","journal-title":"Knee"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.jbspin.2009.12.009","article-title":"Gait analysis as a quantifiable outcome measure in hip or knee osteoarthritis: A systematic review","volume":"77","author":"Ornetti","year":"2010","journal-title":"Jt. Bone Spine"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","article-title":"Gait assessment in Parkinson\u2019s disease: Toward an ambulatory system for long-term monitoring","volume":"51","author":"Salarian","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/7333.928571","article-title":"A reliable gait phase detection system","volume":"9","author":"Pappas","year":"2001","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.gaitpost.2012.06.017","article-title":"Gait phase detection and discrimination between walking\u2013jogging activities using hidden Markov models applied to foot motion data from a gyroscope","volume":"36","author":"Mannini","year":"2012","journal-title":"Gait Posture"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.gaitpost.2012.07.012","article-title":"Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors","volume":"37","author":"Mariani","year":"2013","journal-title":"Gait Posture"},{"key":"ref_12","unstructured":"Seel, T., Sch\u00e4perk\u00f6tter, S., Valtin, M., Werner, C., and Schauer, T. (2013, January 7\u20138). Design and control of an adaptive peroneal stimulator with inertial sensor-based gait phase detection. Proceedings of the 18th Annual International FES Society Conference, San Sebastian, Spain."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/7333.918277","article-title":"Real-time gait event detection for paraplegic FES walking","volume":"9","author":"Skelly","year":"2001","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.3390\/s140101073","article-title":"A wireless flexible sensorized insole for gait analysis","volume":"14","author":"Crea","year":"2014","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.ypmed.2005.07.006","article-title":"Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure","volume":"41","author":"Foster","year":"2005","journal-title":"Prev. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"830","DOI":"10.2340\/16501977-1993","article-title":"Capturing step counts at slow walking speeds in older adults: Comparison of ankle and waist placement of measuring device","volume":"47","author":"Simpson","year":"2015","journal-title":"J. Rehabil. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.gaitpost.2008.01.019","article-title":"Detection of gait events using an F-Scan in-shoe pressure measurement system","volume":"28","author":"Catalfamo","year":"2008","journal-title":"Gait Posture"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Crea, S., De Rossi, S.M.M., Donati, M., Reber\u0161ek, P., Novak, D., Vitiello, N., Lenzi, T., Podobnik, J., Munih, M., and Carrozza, M.C. (September, January 28). Development of gait segmentation methods for wearable foot pressure sensors. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347120"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taborri, J., Palermo, E., Rossi, S., and Cappa, P. (2016). Gait partitioning methods: A systematic review. Sensors, 16.","DOI":"10.3390\/s16010066"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1007\/s40846-016-0151-y","article-title":"Continuous Prediction of Finger Movements Using Force Myography","volume":"36","author":"Kadkhodayan","year":"2016","journal-title":"J. Med. Biol. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S1672-6529(11)60095-4","article-title":"Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map","volume":"9","author":"Li","year":"2012","journal-title":"J. Bionic Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1743-0003-11-2","article-title":"Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities","volume":"11","author":"Xiao","year":"2014","journal-title":"Xiao Menon J. NeuroEng. Rehabil."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.medengphy.2017.01.015","article-title":"Exploration of force myography and surface electromyography in hand gesture classification","volume":"41","author":"Jiang","year":"2017","journal-title":"Med. Eng. Phys."},{"key":"ref_24","first-page":"1","article-title":"Force Exertion Affects Grasp Classification Using Force Myography","volume":"41","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s12938-017-0349-4","article-title":"A preliminary investigation on the utility of temporal features of Force Myography in the two-class problem of grasp vs. no-grasp in the presence of upper-extremity movements","volume":"16","author":"Sadarangani","year":"2017","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_26","first-page":"1","article-title":"Wearable step counting using a force myography-based ankle strap","volume":"4","author":"Chu","year":"2017","journal-title":"J. Rehabil. Assist. Technol. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, X., Chu, K.H., and Menon, C. (2017, January 5\u20138). An easy-to-use wearable step counting device for slow walking using ankle force myography. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122950"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TBME.2003.813539","article-title":"A robust, real-time control scheme for multifunction myoelectric control","volume":"50","author":"Englehart","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/1082-989X.1.1.30","article-title":"Forming inferences about some intraclass correlation coefficients","volume":"1","author":"McGraw","year":"1996","journal-title":"Psychol. Methods"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Endo, K., and Herr, H. (2009, January 12\u201317). A model of muscle-tendon function in human walking. Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA\u201909), Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152622"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1098\/rsif.2010.0084","article-title":"Stance and swing phase costs in human walking","volume":"7","author":"Umberger","year":"2010","journal-title":"J. R. Soc. Interface"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.medengphy.2013.10.004","article-title":"Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations","volume":"36","author":"Rueterbories","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mannini, A., and Sabatini, A.M. (September, January 30). A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091084"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"16212","DOI":"10.3390\/s140916212","article-title":"A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network","volume":"14","author":"Taborri","year":"2014","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/JBHI.2013.2293887","article-title":"Online decoding of hidden Markov models for gait event detection using foot-mounted gyroscopes","volume":"18","author":"Mannini","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:37Z","timestamp":1760194897000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,21]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041279"],"URL":"https:\/\/doi.org\/10.3390\/s18041279","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,21]]}}}