{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T13:42:37Z","timestamp":1790948557701,"version":"4.1.0"},"reference-count":33,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PPP Allowance made available by Health\u00a0Holland, Top Sector Life Sciences & Health"},{"name":"ZonMw"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n               <jats:title>Background<\/jats:title>\n               <jats:p>Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS).<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Methods<\/jats:title>\n               <jats:p>Twenty healthy participants (aged 59\u00a0\u00b1\u00a012 years) and 10 people in the chronic phase after stroke (aged 61\u00a0\u00b1\u00a011 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (\u00b120% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Results<\/jats:title>\n               <jats:p>For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9\u201317 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15\u201324 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm\/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm\/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Conclusions<\/jats:title>\n               <jats:p>Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.<\/jats:p>\n            <\/jats:sec>","DOI":"10.7717\/peerj.16641","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T08:14:50Z","timestamp":1702628090000},"page":"e16641","source":"Crossref","is-referenced-by-count":16,"title":["Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals"],"prefix":"10.7717","volume":"11","author":[{"given":"Carmen","family":"Ensink","sequence":"first","affiliation":[{"name":"Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands"},{"name":"Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katrijn","family":"Smulders","sequence":"additional","affiliation":[{"name":"Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jolien","family":"Warnar","sequence":"additional","affiliation":[{"name":"Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noel","family":"Keijsers","sequence":"additional","affiliation":[{"name":"Department of Research, Sint Maartenskliniek, Nijmegen, the Netherlands"},{"name":"Department of Sensorimotor Neuroscience, Donders institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands"},{"name":"Department of Rehabilitation, Donders institute for Brain, Cognition and Behaviour, Radboud Univeristy Medical Center, Nijmegen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"issue":"1","key":"10.7717\/peerj.16641\/ref-1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v067.i01","article-title":"Fitting Linear Mixed-Effects Models Using lme4","volume":"67","author":"Bates","year":"2015","journal-title":"Journal of Statistical Software"},{"issue":"11","key":"10.7717\/peerj.16641\/ref-2","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.3390\/S19112517","article-title":"Real-time detection of seven phases of gait in children with cerebral palsy using two gyroscopes","volume":"19","author":"Behboodi","year":"2019","journal-title":"Sensors"},{"key":"10.7717\/peerj.16641\/ref-3","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.gaitpost.2017.06.019","article-title":"A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms","volume":"57","author":"Caldas","year":"2017","journal-title":"Gait Posture"},{"issue":"2","key":"10.7717\/peerj.16641\/ref-4","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3390\/s18020394","article-title":"What is the best configuration of wearable sensors to measure spatiotemporal gait parameters in children with cerebral palsy?","volume":"18","author":"Carcreff","year":"2018","journal-title":"Sensors"},{"key":"10.7717\/peerj.16641\/ref-5","doi-asserted-by":"publisher","first-page":"291","DOI":"10.2519\/jospt.2014.4634","article-title":"Spatial-temporal gait characteristics in individuals with hip osteoarthritis: a systematic literature review and meta-analysis","volume":"44","author":"Constantinou","year":"2014","journal-title":"Journal of Orthopaedic & Sports Physical Therapy"},{"key":"10.7717\/peerj.16641\/ref-6","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1093\/GERONA\/GLX254","article-title":"Analysis of free-living gait in older adults with and without parkinson\u2019s disease and with and without a history of falls: identifying generic and disease-specific characteristics","volume":"74","author":"Del\u00a0Din","year":"2019","journal-title":"The Journals of Gerontology. 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