Gait analysis is an important research field in neurodegenerative disease diagnosis. Various IMU-based and vision-based methods have been developed for measuring joint angles during walking process. Dynamic Vision Sensor (DVS) is a neuromorphic engineering product which outputs sequences of events describing the level of brightness change at the pixel level in an asynchronized manner, instead of discrete frames at a predefined frame rate. Due to its microsecond level resolution, DVS is regarded possessing a huge potential to dramatically increase the speed of sensing pipeline for various applications which traditional CMOS camera cannot fulfill. Hence, in this paper we introduce a novel system for gait analysis application; DVS and special markers are used to detect the ankle joint during walking and a method is developed to calculate the desired angle. Further, the robust locally weighted regression is employed as signal smoothing method to reduce the amount of noise. In order to evaluation its performance, an Inertial Measurement Unit (IMU)-based sensory system is also examined and compared in the same experiment and goniometer is used for providing the ground truth. By comparing the captured ankle joint angle trajectories using Dynamic Time Warping (DTW), DVS-based system appears to have higher accuracy in terms of ankle joint angle detection than IMU-based system.
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