Machine learning for muscle activation patterns identification during cyclical movements

Topic:

Cyclical movements are characterized by different muscle activation patterns (onset-offset timings). To interpret correctly the electromyographic (EMG) data, it is important to group cycles sharing similar EMG activation patterns. We developed a method based on hierarchical clustering, able to group cycles showing homogeneous onset-offset activation intervals.

The method was applied to EMG acquired during gait. A by-product of the clustering procedure is the possibility to extract the principal activations of a muscle, that are those activations necessary for the specific muscle contribution to the biomechanical function of walking. Principal activations were also used to define a robust index to quantitative assess the asymmetry of muscle activations during locomotion.

People:

  • Gabriella Balestra (Confirmed Assistant Professor)
  • Samanta Rosati (Assistant Professor with time contract (RTD/b))
  • Gregorio Dotti (Ph.D. Student)
  • Marco Ghislieri (Assistant Professor with time contract (RTD/a))
  • Valentina Agostini (Associate Professor)
  • Marco Knaflitz (Full Professor)

    • Recent publications:

  • An open-source toolbox for enhancing the assessment of muscle activation patterns during cyclical movements
      Gregorio Dotti, Marco Ghislieri, Cristina Castagneri, Valentina Agostini, Marco Knaflitz, Gabriella Balestra and Samanta Rosati
      IOP Physiological Measurement
      10.1088/1361-6579/ad814f
  • Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index
      S. Rosati, M. Ghislieri, G. Dotti, D. Fortunato, V. Agostini, M.Knaflitz, and G. Balestra
      Sensors
      10.3390/s21217186
  • Asymmetry Index in Muscle Activations
      Castagneri, C., Agostini, V., Rosati, S., Balestra, G., Knaflitz, M.
      IEEE Transactions on Neural Systems and Rehabilitation Engineering
      10.1109/TNSRE.2019.2903687
  • Muscle activation patterns during gait: A hierarchical clustering analysis
      S. Rosati, V. Agostini, M. Knaflitz and G. Balestra
      Biomedical Signal Processing and Control
      10.1016/j.bspc.2016.09.017