Evaluation of Gait Phase Detection Delay Compensation Strategies to Control a Gyroscope-Controlled Functional Electrical Stimulation System During Walking

Abstract

Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of stimulation. Wearable sensors provide the necessary input to differentiate gait phases during walking and trigger stimulation. However, gait phase detection is associated with inherent system delays. In this study, five stimulator triggers designed to compensate for gait phase detection delays were tested to determine which trigger most accurately delivered stimulation at the desired times of the gait cycle. Motion capture data were collected on seven typically-developing children while walking on an instrumented treadmill. Participants wore one inertial measurement unit on each ankle and gyroscope data were streamed into the gait phase detection algorithm. Five triggers, based on gait phase detection, were used to simulate stimulation to five muscle groups, bilaterally. For each condition, stimulation signals were collected in the motion capture software via analog channels and compared to the desired timing determined by kinematic and kinetic data. Results illustrate that gait phase detection is a viable finite-state control, and appropriate system delay compensations, on average, reduce stimulation delivery delays by 6.7% of the gait cycle.

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

“…Although detection delays and timing estimation errors are inherent in kinematic-based gait phase detection methods when compared to the “gold standard” method (gait events from force plate data), the method used to estimate gait events themselves may impact the timing differences observed. Quantifying gait event delays [ 5 ] and subsequently providing compensation algorithms for gait event timing errors [ 40 ] are crucial for applications in which gait events serve to trigger assistive applications. This approach allows for appropriate correction and compensation of gait event detection errors to minimize timing errors for the assistive applications [ 40 , 41 ].…”

“…Quantifying gait event delays [ 5 ] and subsequently providing compensation algorithms for gait event timing errors [ 40 ] are crucial for applications in which gait events serve to trigger assistive applications. This approach allows for appropriate correction and compensation of gait event detection errors to minimize timing errors for the assistive applications [ 40 , 41 ].…”

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

“…Such problem identification has not been described in the literature. Nevertheless, there are works in which similar problems are discussed [5,6,[15][16][17][18][19][20][21][22][23][24][25]. We return to this matter in Section 1.5 (Related Work).…”

A Neuro-Prosthetic Device for Substituting Sensory Functions during Stance Phase of the Gait

“…For complicated systems with FES applications, more accurate gait event detection is needed, which has encouraged extensive research in this field to propose different methods and algorithms. As an example, Zahradka et al [16] designed a real-time system using two inertial measurement units to detect seven phases of the gait with very high reliability.…”