Measurement of foot placement and its variability with inertial sensors

Abstract

Gait parameters such as stride length, width, and period, as well as their respective variabilities, are widely used as indicators of mobility and walking function. Foot placement and its variability have thus been applied in areas such as aging, fall risk, spinal cord injury, diabetic neuropathy, and neurological conditions. But a drawback is that these measures are presently best obtained with specialized laboratory equipment such as motion capture systems and instrumented walkways, which may not be available in many clinics and certainly not during daily activities. One alternative is to fix Inertial Measurement Units (IMUs) to the feet or body to gather motion data. However, few existing methods measure foot placement directly, due to drift associated with inertial data. We developed a method to measure stride-to-stride foot placement in unconstrained environments, and tested whether it can accurately quantify gait parameters over long walking distances. The method uses ground contact conditions to correct for drift, and state estimation algorithms to improve estimation of angular orientation. We tested the method with healthy adults walking over-ground, averaging 93 steps per trial, using a mobile motion capture system to provide reference data. We found IMU estimates of mean stride length and duration within 1% of motion capture, and standard deviations of length and width within 4% of motion capture. Step width cannot be directly estimated by IMUs, although lateral stride variability can. Inertial sensors measure walks over arbitrary distances, yielding estimates with good statistical confidence. Gait can thus be measured in a variety of environments, and even applied to long-term monitoring of everyday walking.

Smartphone and Portable Media Device: A Novel Pathway toward the Diagnostic Characterization of Human Movement

“…Sensor fusion involves the tandem operation of multiple inertial sensors, such as an accelerometer and gyroscope. With the combined signal of both accelerometer and gyroscope signal the actual spatial location in terms of displacement, velocity, and acceleration of the sensor mounting position can be determined [57][58][59]. For example, the spatial representation of foot displacement can be ascertained during gait [57].…”

“…Traditional sensor fusion however requires a considerable sampling rate that exceeds the feasible sampling threshold for smartphones and portable media devices as wearable and wireless systems [1][2][3]57]. Post-processing resources are generally not portable, and techniques, such as the Kalman filter are required to serve as an orientation filter [57][58][59].…”

“…Post-processing resources are generally not portable, and techniques, such as the Kalman filter are required to serve as an orientation filter [57][58][59]. An orientation filter that satisfies the computational bounds associated with smartphones and portable media devices would be desirable for sensor fusion applications.…”

A New Application of Smart Walker for Quantitative Analysis of Human Walking

“…However, some steps do not lead to a high-peak forward acceleration, and hence they are not counted although there is displacement during these periods. A recent study [32] used thresholds on the magnitude of the gyroscope and accelerometer signals to identify the zero velocity instant and regarded it as the end of a step. Our walker also uses the gyrometer data to detect the walking stride.…”

Click-on-and-Play human motion capture using wearable sensors

“…Stride lengths and lateral stride variability can however be estimated, as described for example by Rebula et al in [56] and Schepers et al in [70].…”