{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:15:45Z","timestamp":1775819745491,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Hubei Electric Power Co., Ltd.","award":["521520240009"],"award-info":[{"award-number":["521520240009"]}]},{"name":"Key Research and Development Program of Hubei Province","award":["2024BCB007"],"award-info":[{"award-number":["2024BCB007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion\/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context.<\/jats:p>","DOI":"10.3390\/s26082346","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:20:16Z","timestamp":1775816416000},"page":"2346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Motion Intention Recognition Method for Lower-Limb Exoskeleton Assistance in Ultra-High-Voltage Transmission Tower Climbing"],"prefix":"10.3390","volume":"26","author":[{"given":"Haoyuan","family":"Chen","sequence":"first","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Robotics, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhan","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Hu","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingqi","family":"Wu","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanhong","family":"Shi","sequence":"additional","affiliation":[{"name":"State Grid Hubei Electric Power Co., Ltd., Exta High Voltage Company, Wuhan 430050, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4224-8595","authenticated-orcid":false,"given":"Zhao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Robotics, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"ref_1","first-page":"66","article-title":"UAV-based non-contact corrosion detection platform for ultra-high-voltage transmission towers","volume":"46","author":"Zhong","year":"2025","journal-title":"Corros. 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