{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:08:30Z","timestamp":1777367310073,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Knee joint moment is an important biomechanical parameter for sports assessment, rehabilitation monitoring, and human\u2013machine interaction. However, direct measurement is often restricted to laboratory-based settings. Surface electromyography (sEMG) offers a non-invasive alternative for indirect joint moment estimation, but many existing deep learning models remain too computationally demanding for potential wearable edge deployment. To address this gap, this study proposes Topo2DCNN-LSTM, a lightweight two-dimensional (2D) convolutional neural network model, designed for sagittal-plane knee flexion moment estimation. The model used a feature-based sequential representation, transforming raw sEMG signals into compact Root Mean Square (RMS) feature frames. The input was processed by a lightweight 2D convolutional neural network (CNN) encoder and paired with long short-term memory (LSTM) units. The model was trained on a public walking dataset of healthy subjects with synchronized sEMG and joint kinetics at two treadmill speeds. When compared with selected deep learning baselines, the quantized model achieved a mean RMS Error of 0.088 \u00b1 0.020 Nm\/kg at 1.2 m\/s and 0.114 \u00b1 0.034 Nm\/kg at 1.8 m\/s. On a SparkFun Thing Plus\u2013SAMD51, it achieved an average inference latency of 28 ms using 71,316 bytes of random-access memory (RAM) and 257,172 bytes of flash. These results support its use as a proof of concept for personalized unilateral knee moment estimation with isolated on-device inference feasibility under resource-constrained and limited walking conditions.<\/jats:p>","DOI":"10.3390\/s26082500","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T08:24:23Z","timestamp":1776673463000},"page":"2500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6709-2818","authenticated-orcid":false,"given":"Yuanzhi","family":"Zhuo","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7877-1679","authenticated-orcid":false,"given":"Adrian","family":"Pranata","sequence":"additional","affiliation":[{"name":"School of Health and Biomedical Science, RMIT University, Bundoora, VIC 3082, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-6148","authenticated-orcid":false,"given":"Chi-Tsun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Mechanical, Manufacturing and Mechatronic Engineering Department, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4766-3042","authenticated-orcid":false,"given":"Toh Yen","family":"Pang","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1016\/j.arth.2012.02.009","article-title":"The Knee Adduction Moment during Gait Is Associated with the Adduction Angle Measured during Computer-Assisted Total Knee Arthroplasty","volume":"27","author":"Roda","year":"2012","journal-title":"J. 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