  The use of 3D cameras for gait analysis has been highly questioned due to the
low accuracy they have demonstrated in the past. The objective of the study
presented in this paper is to improve the accuracy of the estimations made by
robot-mounted 3D cameras in human gait analysis by applying a supervised
learning stage. The 3D camera was mounted in a mobile robot to obtain a longer
walking distance. This study shows an improvement in detection of kinematic
gait signals and gait descriptors by post-processing the raw estimations of the
camera using artificial neural networks trained with the data obtained from a
certified Vicon system. To achieve this, 37 healthy participants were recruited
and data of 207 gait sequences were collected using an Orbbec Astra 3D camera.
There are two basic possible approaches for training: using kinematic gait
signals and using gait descriptors. The former seeks to improve the waveforms
of kinematic gait signals by reducing the error and increasing the correlation
with respect to the Vicon system. The second is a more direct approach,
focusing on training the artificial neural networks using gait descriptors
directly. The accuracy of the 3D camera was measured before and after training.
In both training approaches, an improvement was observed. Kinematic gait
signals showed lower errors and higher correlations with respect to the ground
truth. The accuracy of the system to detect gait descriptors also showed a
substantial improvement, mostly for kinematic descriptors rather than
spatio-temporal. When comparing both training approaches, it was not possible
to define which was the absolute best. Therefore, we believe that the selection
of the training approach will depend on the purpose of the study to be
conducted. This study reveals the great potential of 3D cameras and encourages
the research community to continue exploring their use in gait analysis.
