A Sensitive and Accurate Walking Speed Prediction Method Using Ankle Torque Estimation for a User-Driven Treadmill Interface

To achieve safe and immersive interface with a user-driven treadmill (UDT), robustness of the user position must be ensured by sensitively estimating and accurately converging to the intentional walking speed (IWS). The existing IWS estimation using a linear observer with the cart model (
$1^{\mathrm {st}}$
order dynamics) can exponentially converge to the true IWS. However, when the estimation sensitivity is increased by increasing the gain, this method causes severe postural instability due to the generation of excessive anomalous forces. Thus, the existing method has an implicit limitation with regards to increasing the position robustness because of the postural instability issues. In this paper, to simultaneously achieve sensitive and accurate IWS estimation while reducing postural instability on a UDT, in addition to the cart model, we have also utilized the inverted pendulum-based gait model (IPGM) as a
$2^{\mathrm {nd}}$
order dynamic to estimate the intentional walking acceleration (IWA) generated by the ankle torque. Thus, the proposed IWS prediction method uses the cart model for accurate convergence to IWS and the IPGM to follow sensitively the change in the IWS. In the proposed method, the internal states of the existing observers applied to the
$1^{\mathrm {st}}$
and
$2^{\mathrm {nd}}$
order dynamics are shared recursively to estimate the ankle torque acting as a disturbance for the IPGM and to sensitively predict the change in the IWS. Experiments show that the proposed method can significantly facilitate the users in following a profile of desired walking speeds more accurately than the existing IWS estimation method under the same position robustness setup.