  Gait analysis (GA) has been widely used in physical activity monitoring and
clinical contexts, and the estimation of the spatial-temporal gait parameters
is of primary importance for GA. With the quick development of smart tiny
sensors, GA methods based on wearable devices have become more popular
recently. However, most existing wearable GA methods focus on data analysis
from inertial sensors. In this paper, we firstly present a two-foot-worn
in-shoe system (Gaitboter) based on low-cost, wearable and multimodal sensors'
fusion for GA, comprising an inertial sensor and eight film-pressure sensors
with each foot for gait raw data collection while walking. Secondly, a GA
algorithm for estimating the spatial-temporal parameters of gait is proposed.
The algorithm fully uses the fusion of two kinds of sensors' signals: inertial
sensor and film-pressure sensor, in order to estimate the spatial-temporal gait
parameters, such as stance phase, swing phase, double stance phase, cadence,
step time, stride time, stride length, velocity. Finally, to verify the
effectiveness of this system and algorithm of the paper, an experiment is
performed with 27 stoke patients from local hospital that the spatial-temporal
gait parameters obtained with the system and the algorithm are compared with a
GA tool used in medical laboratories. And the experimental results show that it
achieves very good consistency and correlation between the proposed system and
the compared GA tool.
