title: On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population.
abstract: Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.
  
  
  
  Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.
authors.affiliation: Biomedical Engineering Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada; and navids@mail.ubc.ca.
authors.forename: Navid
authors.identifier: 0000-0003-3839-2583
authors.initials: N
authors.lastname: Lambert-Shirzad
authors.affiliation: Department of Mechanical Engineering University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: H F Machiel
authors.initials: HF
authors.lastname: Van der Loos
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004576:Electromyography; D005260:Female; D006801:Humans; D007596:Joints / Q000294:innervation; D008297:Male; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D025341:Principal Component Analysis; D055815:Young Adult
keywords: independent component analysis; kinematic synergies; matrix factorization; motor control; motor coordination; muscle synergies; nonnegative matrix factorization; principal component analysis