{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:19:30Z","timestamp":1785421170736,"version":"3.56.0"},"reference-count":85,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Land Schleswig-Holstein"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Many algorithms use 3D accelerometer and\/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall \u226592%, precision \u226597%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (\u22120.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.<\/jats:p>","DOI":"10.3390\/s22103859","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:18:11Z","timestamp":1653005891000},"page":"3859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2507-0924","authenticated-orcid":false,"given":"Robbin","family":"Romijnders","sequence":"first","affiliation":[{"name":"Department of Neurology, Kiel University, 24105 Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-7761","authenticated-orcid":false,"given":"Elke","family":"Warmerdam","sequence":"additional","affiliation":[{"name":"Innovative Implant Development (Fracture Healing), Division of Surgery, Saarland University, 66421 Homburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4813-3868","authenticated-orcid":false,"given":"Clint","family":"Hansen","sequence":"additional","affiliation":[{"name":"Department of Neurology, Kiel University, 24105 Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerhard","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Institute of Electrical Engineering and Information Technology, Faculty of Engineering, Kiel University, 24143 Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5945-4694","authenticated-orcid":false,"given":"Walter","family":"Maetzler","sequence":"additional","affiliation":[{"name":"Department of Neurology, Kiel University, 24105 Kiel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S1474-4422(06)70678-0","article-title":"Neurological gait disorders in elderly people: Clinical approach and classification","volume":"6","author":"Snijders","year":"2007","journal-title":"Lancet Neurol."},{"key":"ref_2","first-page":"44","article-title":"The importance of measuring human gait","volume":"19","author":"Hodgins","year":"2008","journal-title":"Med. 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