  In this work, we present a novel framework for on-line human gait stability
prediction of the elderly users of an intelligent robotic rollator using Long
Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range
Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based
approach is used for the upper body pose estimation. The detected pose is used
for estimating the body Center of Mass (CoM) using Unscented Kalman Filter
(UKF). An Augmented Gait State Estimation framework exploits the LRF data to
estimate the legs' positions and the respective gait phase. These estimates are
the inputs of an encoder-decoder sequence to sequence model which predicts the
gait stability state as Safe or Fall Risk walking. It is validated with data
from real patients, by exploring different network architectures,
hyperparameter settings and by comparing the proposed method with other
baselines. The presented LSTM-based human gait stability predictor is shown to
provide robust predictions of the human stability state, and thus has the
potential to be integrated into a general user-adaptive control architecture as
a fall-risk alarm.
