Human Gait Kinematic Estimation based on Joint Data Acquisition and Analysis from IMU and Depth-Sensing Camera

Gait analysis is very important on applications of clinical rehabilitation of patients of stroke or spinal cord injuries. In this paper, the acquisition of spatiotemporal and angular parameters are obtained by using a Depth Sensing camera, Inertial Measurement Units (IMUs), and Vicon Motion Capture systems (Vicon MoCap) which is considered as the gold standard in gait data acquisition, simultaneously. However, Vicon MoCap is costly and is not easily accessible in the Philippines. This paper proposes a method for gait analysis through the development of an integrated system for gait data processing, consisting of Depth Sensing Camera and IMU sensors as an alternative to Vicon MoCap. A deep learning algorithm called Bayesian Regularization Artificial Neural Network (BRANN) is used to synthesize the data from Depth Sensing Camera and IMU due to its capabilities in complex and non-linear problems with considerable time. The output of the integrated system, in comparison to the Depth Sensing Camera and IMUs, showed an 85.2647% to 99.5636% reduction for the Depth Sensing Camera and an 82.1329% to 99.6551% reduction for the IMU in the Mean Squared Error. It also showed an increase in the number significant parameters using hypothesis test.