{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:12:46Z","timestamp":1772989966051,"version":"3.50.1"},"reference-count":19,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":321,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M740662"],"award-info":[{"award-number":["2023M740662"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Accurate gait speed estimation is vital for mobility assessment, rehabilitation monitoring, and intelligent assistive systems. However, existing methods often struggle under non\u2010ideal conditions such as abrupt speed changes, asymmetric steps, or environmental disturbances. This study proposes a reliable framework for estimating gait speed,\u00a0which integrates inertial measurement unit (IMU) signals with a hybrid deep learning model based on convolutional neural networks (CNNs) and bidirectional long short\u2010term memory (BiLSTM) networks. Six IMUs were placed on bilateral thighs, calves, and ankles to capture comprehensive lower\u2010limb motion data. After noise filtering, gait cycle segmentation, and Z\u2010score normalisation, features were extracted across time, frequency, and time\u2010frequency domains using Fast Fourier and Continuous Wavelet Transforms (FFT and CWT). A two\u2010stage feature selection strategy combining Random Forest (RF) and Principal Component Analysis (PCA) reduced dimensionality while preserving discriminative power. The CNN\u2010BiLSTM model was trained and tested on data from eight healthy subjects under variable\u2010speed treadmill walking. Results showed superior performance, with an average\n                    <jats:italic>\n                      R\n                      <jats:sup>2<\/jats:sup>\n                    <\/jats:italic>\n                    of 95.2% and a root mean square error (RMSE) of 0.0301 m\/s, outperforming convolutional neural network (CNN), long short\u2010term memory (LSTM), GRU, and backpropagation (BP) models. Sensor ablation confirmed the value of multi\u2010site integration, and real\u2010time tests validated online applicability. This work offers a reliable, high\u2010precision solution for gait speed estimation under non\u2010ideal conditions, with potential for wearable gait analysis and intelligent rehabilitation.\n                  <\/jats:p>","DOI":"10.1049\/htl2.70029","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:45:07Z","timestamp":1763473507000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Human Gait Speed Recognition Under Non\u2010Ideal Conditions for Suspension\u2010Assisted Walking Systems"],"prefix":"10.1049","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0509-2130","authenticated-orcid":false,"given":"Yongfei","family":"Feng","sequence":"first","affiliation":[{"name":"Academy For Engineering and Technology Fudan University  Shanghai China"},{"name":"Faculty of Mechanical Engineering and Mechanics Ningbo University  Ningbo China"},{"name":"Ningbo Key Laboratory of Aging Health Equipment and Service Technology Ningbo Polytechnic  Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiao","family":"Sang","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering and Mechanics Ningbo University  Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Tian","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Aging Health Equipment and Service Technology Ningbo Polytechnic  Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengcai","family":"Wang","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Aging Health Equipment and Service Technology Ningbo Polytechnic  Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangyan","family":"Dong","sequence":"additional","affiliation":[{"name":"Academy For Engineering and Technology Fudan University  Shanghai 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