Speed-adaptive control of functional electrical stimulation for dropfoot correction

Abstract

Background: Functional electrical stimulation is an important therapy technique for dropfoot correction. In order to achieve natural control, the parameter setting of FES should be associated with the activation of the tibialis anterior.

Methods: This study recruited nine healthy subjects and investigated the relations of walking speed with the onset timing and duration of tibialis anterior activation. Linear models were built for the walking speed with respect to these two parameters. Based on these models, the speed-adaptive onset timing and duration were applied in FES-assisted walking for nine healthy subjects and ten subjects with dropfoot. The kinematic performance of FES-assisted walking triggered by speed-adaptive stimulation were compared with those triggered by the heel-off event, and no-stimulation walking at different walking speeds.

Results: Higher ankle dorsiflexion angle was observed in heel-off stimulation and speed-adaptive stimulation conditions than that in no-stimulation walking condition at all the speeds. For subjects with stroke, the ankle plantarflexion angle in speed-adaptive stimulation condition was similar to that in no-stimulation walking condition, and it was significant larger than that in heel-off stimulation condition at all speeds.

Conclusions: The improvement in ankle dorsiflexion without worsening ankle plantarflexion in speed-adaptive stimulation condition could be attributed to the appropriate stimulation timing and duration. These results provide evidence that the proposed stimulation system with speed-related parameters is more physiologically appropriate in dropfoot correction, and it may have great potential value in future clinical applications.

Trial registration: Medical Ethics Committee of Guangdong Work Injury Rehabilitation Center, AF/SC-07/2016.22 . Registered 26 May 2016.

Keywords: Dropfoot; Electromyography; Functional electrical stimulation; Walking speed.

Fig. 1

a The experiment setup of SAS condition; b one healthy subject on the treadmill for system evaluation; c the position of five markers on the right leg

Fig. 2

Working flowchart of the SAS control system

Fig. 3

The EMG signal of TA after amplification at a gain of 4000 and the footswitch signals at the speed of 1 m/s of one healthy subject

Fig. 4

Linear models of walking speed with time interval and EMG duration. R: negative linear correlation; P: significantly linear correlation; The equation is the negative linear model established

Fig. 5

a Ankle angles (mean ± std) during the gait cycle for nine healthy subjects at 0.9 m/s; b knee angles (mean ± std) during the gait cycle for nine healthy subjects at 0.9 m/s; c ankle angles (mean ± std) during the gait cycle for ten post-stroke subjects at free speed; d knee angles (mean ± std) during the gait cycle for ten post-stroke subjects at free speed

Fig. 6

Nine healthy subjects’ results for: a Ankle plantarflexion angles at toe-off event; b maximum ankle dorsiflexion angles during swing phase; c peak knee flexion angles during swing phase; *: significant difference from NS (P < 0.05); ☨: significant difference from HOS (P < 0.05)

Fig. 7

The results of ten subjects with stroke: a ankle plantarflexion angles at toe-off event, b maximum ankle dorsiflexion angles during swing phase, and c peak knee flexion angles during swing phase, *: significant difference from NS (P < 0.05), †: significant difference from HOS (P < 0.05)

Fig. 8

Comparison between healthy subjects and subjects with stroke: a Ankle plantarflexion angles at toe-off event; b maximum ankle dorsiflexion angles during swing phase; c peak knee flexion angles during swing phase; *: P < 0.05. The error bars represented the standard deviations