title: Force Modelling of Upper Limb Biomechanics Using Ensemble Fast Orthogonal Search on High-Density Electromyography.
abstract: An important quality of upper limb force estimation is the repeatability and worst-case performance of the estimator. The following paper proposes a methodology using an ensemble learning technique coupled with the fast orthogonal search (FOS) algorithm to reliably predict varying isometric contractions of the right arm. This method leverages the rapid and precise modelling offered by FOS combined with a univariate outlier detection algorithm to dynamically combine the output of numerous FOS models. This is performed using high-density surface electromyography (HD-SEMG) obtained from three upper-arm muscles, the biceps brachii, triceps brachii and brachioradialis. This method offers improved performance over other HD-SEMG and SEMG based force estimators, with a substantial reduction in the number of channels required.
authors.forename: Gregg
authors.initials: G
authors.lastname: Johns
authors.forename: Evelyn
authors.initials: E
authors.lastname: Morin
authors.forename: Keyvan
authors.initials: K
authors.lastname: Hashtrudi-Zaad
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm / Q000502:physiology*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D007537:Isometric Contraction / Q000502:physiology*; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013314:Stress, Mechanical; D055815:Young Adult