Abstract—The analysis between finger movements is an important aspect of biophysics and rehabilitation. The study aims to evaluate the distinctive muscle activities between finger movements through the help of High-Density Electromyographic (EMG) signals for increased myoelectric control of soft robotic hand. 64-channel EMG signal was recorded during individual finger isometric task for 5 healthy subjects. Raw, single differential and double differential EMG signals across the 2D array was analyzed. Spatial image of theses signals for the 4 different finger movement demonstrated multiple distinctive properties, the major distinction. Feature set of six distinct features was calculated for the array of EMG signals to quantitatively differentiation between finger movements. Centroid of these feature set acquired different 3D space indicating differences in the finger movements. This indicated that HDEMG could be used for differentiating finger movements and could be used as a method for classification algorithm for increased myoelectric finger control in future.
Index Terms—high density EMG, Electromyography (EMG), spatial analysis, myoelectric finger control
Cite: Prabhav Mehra, Manya Dave, Ahsan Khan, and Raymond K. Y. Tong, "Spatial Mapping and Feature Analysis for Individual Finger Movements Using High Density Electromyography: Preliminary Study," Journal of Image and Graphics, Vol. 8, No. 3, pp. 75-79, September 2020. doi: 10.18178/joig.8.3.75-79
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