Leveraging high-density EMG to investigate bipolar electrode placement
for gait prediction models
Abstract
For the control of wearable robotics, it is critical to obtain a
prediction of the user’s motion intent with high accuracy.
Electromyography (EMG) recordings have often been used as inputs for
these devices, however bipolar EMG electrodes are highly sensitive to
their location. Positional shifts of electrodes after training gait
prediction models can therefore result in severe performance
degradation.
This study uses high-density EMG electrodes to simulate various bipolar
electrode signals from four leg muscles during steady-state walking. The
bipolar signals were ranked based on the consistency of the
corresponding EMG envelope’s activity and timing across gait cycles.
The locations were then compared by evaluating the performance of an
offline Temporal Convolutional Network (TCN) that mapped EMG signals to
knee angles. The results showed that electrode locations with consistent
EMG envelopes resulted in greater prediction accuracy compared to
hand-aligned placements (p<0.01). However, performance gains
through this process were limited, and did not resolve the position
shift issue.
Instead of training a model for a single location, we showed that
randomly sampling bipolar combinations across the high-density EMG grid
during training mitigated this effect. Models trained with this method
generalised over all positions, and achieved 70% less prediction error
than location specific models over the entire area of the grid.
Therefore, the use of high-density EMG grids to build training datasets
could enable the development of models robust to spatial variations, and
reduce the impact of muscle-specific electrode placement on accuracy.