Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPytho