Article

Real-time gait event detection is a requirement for functional electrical
stimulation and gait biofeedback. This gait event detection should ideally be
achieved using an ambulatory system of durable, lightweight, low-cost sensors.
Previous research has reported issues with durability in footswitch systems.
Therefore, this study describes the development and assessment of novel detection
algorithms using footswitch and accelerometer sensors on 12 healthy individuals.
Subjects were equipped with one force sensitive resistor on the heel, one
accelerometer at the foot, and one accelerometer at the knee. Subjects performed
10, 8-m walking trials in each of three conditions: normal, slow, and altered
(reduced knee ROM) walking. Data from a subset of four subjects were used to
develop prediction algorithms for initial contact (IC). Subsequently, these
algorithms were tested on the remaining eight subjects against standard
forceplate IC data (threshold of 5 N on a rising edge). The footswitch force
threshold algorithm was most accurate for IC detection (mean absolute error of
2.4+/-2.1 ms) and was significantly more accurate (p<0.001) than the optimal
accelerometer algorithm (mean absolute error of 9.5+/-9.0 ms). The optimal
accelerometer algorithm used data from both accelerometers, with IC determined
from the second derivative of foot fore-aft acceleration. The error results for
footswitch and accelerometer algorithms are lower (approximately 60%) than in
previous research on ambulatory real-time gait event detection systems.
Currently, footswitch systems must be recommended over accelerometer systems for
accurate detection of IC, however, further research into accelerometer algorithms
is merited due to its advantages as a durable, low-cost sensor.

Langue : ANGLAIS