Detection thresholds for electrostimulation combined with robotic leg support in sub-acute stroke patients

Stroke is one of the leading causes of disability in adults in the European Union. It often leads to motor impairments, such as a hemiparetic lower extremity. Research indicates that early task-specific and intensive training promotes neuroplasticity and leads to recovery and/or compensation. One way to provide intensive training early after a stroke is via robot-supported training. A rehabilitation robot was designed by Life Science Robotics (Aalborg, Denmark) that can provide continuous repetitive movements of the hip, knee, and/or ankle in e.g., a lying position. In order to emphasize active contribution by the patient, actively triggered electrical stimulation (via muscle activation) can be combined with robotic assistance. The current study aims to compare different threshold estimation methods for detection of movement intention from muscle activity for actively triggered electrical stimulation during robot-supported leg movement in stroke patients. Three sub-acute stroke patients were included for a single measurement session. They performed knee extension and/or ankle dorsal flexion with four different threshold estimation methods to assess the intention detection threshold to initiate electrostimulation. The thresholds were based on the resting level of muscle activity (of m. rectus femoris or m. tibialis anterior) plus two or three times the standard deviation of the average resting value, or the resting level plus 5% or 10% of the peak muscle activity during a maximal voluntary contraction. The results showed that the method based on the resting muscle activity plus two times the standard deviation was the most stable across the three included stroke patients. This method had a detection success rate of 86.7% and was experienced as moderately comfortable. In conclusion, performing knee extension and/or ankle dorsal flexion with electromyography triggered electrostimulation is feasible in sub-acute stroke patients. Muscle activity-triggered electrostimulation combined with robotic support based on a threshold of resting levels plus two times the standard deviation seems to detect movement initiation most consistently in this small sample of sub-acute stroke patients.