{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:25:11Z","timestamp":1775690711357,"version":"3.50.1"},"reference-count":30,"publisher":"Emerald","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,25]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to address the lag in real-time human motion tracking for weight-loading lower-limb exoskeletons by proposing a novel movement prediction method. The purpose is to enhance exoskeleton responsiveness through accurate prediction of lower-limb movement (LLM), enabling seamless human\u2013robot interaction in industrial scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>An adaptive temporal movement primitives (ATMPs)-based neuromorphic framework is developed, inspired by alpha motor neuron mechanisms. The method decomposes LLM into three primitive types (W-TMPs, S-TMPs and B-TMPs) and uses online adaptive algorithms (MDA-OGF) for real-time parameter tuning. A bilateral synchronization mechanism ensures robustness across locomotion modes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Experimental validation demonstrated a prediction horizon of 148 ms with 4.25% root mean square error, outperforming the state-of-the-art methods. The algorithm showed robustness across seven locomotion modes and three transitional modes, with transient PRMSE &amp;lt;= 11.1% during mode switches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This work introduces a neuroscience-inspired ATMPs framework that combines the advantages of different prediction methods, achieving a balance between prediction accuracy and prediction horizon. The method\u2019s scalability to diverse wearable systems with high-frequency joint angle sensing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ir-11-2024-0516","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:30:11Z","timestamp":1743039011000},"page":"866-876","source":"Crossref","is-referenced-by-count":3,"title":["Lower-limb movements prediction method based on adaptive temporal movement primitives for weight-loading exoskeletons"],"prefix":"10.1108","volume":"52","author":[{"given":"Yinan","family":"Miao","sequence":"first","affiliation":[{"name":"Tsinghua University School of Mechanical Engineering, , Beijing, , and School of Automation Science and Electrical Engineering, Beihang University, Beijing,","place":["China China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beihang University School of Automation Science and Electrical Engineering, , Beijing,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rufei","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Mechanical Equipment , Beijing,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxun","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Mechanical Equipment , Beijing,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoping","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University School of Automation Science and Electrical Engineering, , Beijing,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingjian","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University School of Automation Science and Electrical Engineering, , Beijing,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"2025121104215485700_ref001","article-title":"Design and impedance control of a hydraulic robot for paralyzed people","volume-title":"8th RSI International Conference on Robotics and Mechatronics, ICRoM 2020.","author":"Abbasi Moshaei","year":"2020"},{"issue":"1","key":"2025121104215485700_ref002","article-title":"Analytical model of hand phalanges desired trajectory for rehabilitation and design a sliding mode controller based on this model","volume":"20","author":"Abbasi Moshaei","year":"2020","journal-title":"Modares Mechanical Engineering"},{"key":"2025121104215485700_ref003","first-page":"1","article-title":"Movenet: a deep neural network for joint profile prediction across variable walking speeds and slopes","volume":"70","author":"Bajpai","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"3","key":"2025121104215485700_ref004","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1108\/IR-03-2018-0054","article-title":"Learning and planning of stair ascent for lower-limb exoskeleton systems","volume":"46","author":"Chen","year":"2019","journal-title":"Industrial Robot: The International Journal of Robotics Research and Application"},{"key":"2025121104215485700_ref005","first-page":"1","article-title":"Gait prediction and variable admittance control for lower limb exoskeleton with measurement delay and extended-state-observer","volume-title":"IEEE Trans. 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