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We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.<\/jats:p>","DOI":"10.1007\/s11517-021-02466-z","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:02:32Z","timestamp":1642172552000},"page":"683-699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques"],"prefix":"10.1007","volume":"60","author":[{"given":"Chiako","family":"Mokri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Bamdad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2151-5180","authenticated-orcid":false,"given":"Vahid","family":"Abolghasemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"2466_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cmpb.2017.08.008","volume":"151","author":"M Trombetta","year":"2017","unstructured":"Trombetta M, Bazzanello Henrique PP, Brum MR, Colussi EL, De Marchi ACB, Rieder R (2017) Motion rehab ave 3d: a vr-based exergame for post-stroke rehabilitation. 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