Guffanti, Diego
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Guffanti, Diego
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Quito
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57200385407

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Supervised learning for improving the accuracy of robot-mounted 3D camera applied to human gait analysis

2024 , Guffanti, Diego , Brunete A. , Hernando M. , Álvarez D. , Rueda J. , Navarro E.

Background and Objective: 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. Methods: 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 and both have been studied in order to see which one achieves a better result. The artificial neural network can be trained either to obtain more accurate kinematic gait signals or to improve the gait descriptors obtained after initial processing. 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. Results: the accuracy of the 3D camera to objectify human gait was measured before and after training. In both training approaches, a considerable 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. Conclusions: supervised learning improves the accuracy of 3D cameras but 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. © 2024 The Authors

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RoboGait: a robotic system for non-invasive gait analysis. [RoboGait: sistema robótico no invasivo para el análisis de la marcha humana]

2024 , Álvarez D. , Guffanti, Diego , Brunete A. , Hernando M. , Gambao E.

The most common methods used in gait analysis laboratories are systems based on the use of markers and/or sensors positioned all over the patient’s body while performing a walking test. These approaches usually require individual calibration, a long time to set up the patient, and, therefore, discomfort of the users. Besides, some of the methods can only be performed in specific small scenarios that need to be previously set-up with external sensors. The presented system, RoboGait, is designed to overcome these problems while maintaining a good performance in terms of quality of the measurements provided. RoboGait is a mobile robotic platform that moves in front of a patient that is walking. The system measures the configuration of the patient’s body using an RGBD camera mounted on the top. Initial measurements provided by the camera are processed using an Artificial Neural Network that improves the estimated kinematic and spatio-temporal signals of the patient’s movement. This paper shows the effectiveness of the system by comparing with a validated method that uses a Vicon® system. Then, the work shows the usefulness of RoboGait in a clinical environment by using it to classify healthy and pathological gaits. In this case, the results have been compared to a reference system based on inertial sensors called Xsens®. The results show a great potential for the use of RoboGait for clinical patient assessment and monitoring, and for pathology identification. © 2024 Universidad Politecnica de Valencia.. All rights reserved.

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