Abstract:
Terrain classi cation is an active area of research in robotics and autonomous
o -road vehicle driving. However, this problem has barely been studied with
the inertial data of human gait. In this thesis, estimation and classi cation of
the type of terrain has been performed on which a person has walk. For this
purpose we have recorded the gait patterns with inertial sensors of normal
human walk. We select six di erent terrain types with variation in hardness
and friction. We record accelerations and angular velocities of 40 subjects
with two smartphone embedded (MPU-6500) and smart-watch embedded
(K6DS3TR) at three di erent body locations (chest, lower back and right
ankle). The recorded data was segmented with two di erent approaches
namely stripe based segmentation and step based segmentation of signal.
Then we computed a total of 200 spatio-spectral features from segmented
strides and steps. We trained two machine learning classi ers namely support
vector machine and random forest with 10-fold cross validation method
for classi cation tasks. We perform classi cation experiments on indooroutdoor,
hard-soft, terrain classi cation as well as with class combinations of
binary, ternary, quaternary, quinary and senary terrain classes. We achieved
indoor-outdoor and hard-soft terrain classi cation results of 97% and 92% respectively.
Furthermore, the classi cation results of 96%, 94%, 92%, 90% and
89% were achieved for binary, ternary, quaternary, quinary and senary class
classi cation. From these results we can conclude that terrain classi cation
can be performed with higher accuracy from 6D accelerations and angular
velocities of strides and single walking steps. The results show that stride
segmented features perform better than step segmented features. Moreover,
the problem at hand can be solved from any of the given sensor location,
senor type and invariant types of shoes.
