Abstract
Human motion monitoring is important in applications of movies, animation, sport training, physical rehabilitation, and human-robot interaction. There is a demand for a simple and easy-to-use method to recognize motions of limbs and trunk. In this paper, we developed a wearable velocity tracking device using two orthogonally-placed micro flow sensors to implement three-dimensional motion velocity measurement. In addition, we proposed a functional link artificial neural network (FLANN) model to extract trunk velocity and relative limb velocity from an absolute limb motion detected by the wearable tracking device according to their different dynamic features. Experiments were conducted to validate the effectiveness of the velocity tracking methodology. Results showed the proposed method with wearable device enables real-time measurements of motion velocities of limb and trunk, which were free of accumulated error, robust for dynamic walking and running, and simple to use.
Highlights
Human motion recognition has become significant in applications of movies, animation, sport training, physical rehabilitation for disabled, and human-robot/human-computer interaction
We propose a new method to measure trunk velocity, relative limb velocity and absolute limb velocity in human walking and running at the same time using single wearable motion tracking device worn on human limb
We propose a functional link artificial neural network (FLANN) model to extract trunk velocity and relative limb velocity from the absolute limb velocity measured by single tracking device worn on the wrist, which significantly simplifies the setup of the wearable velocity tracking system
Summary
Human motion recognition has become significant in applications of movies, animation, sport training, physical rehabilitation for disabled, and human-robot/human-computer interaction. Among a lot of kinematic and kinetic parameters of human motion, the motion velocities of trunk and limbs are very important in practice. Center-of-mass (CoM) velocity, which usually uses the velocity of trunk instead, is necessary for balance control of bipedal robots [1] and estimation of mechanical energetic cost [2], whereas the limb velocity plays an important role in evaluating fine motor function, such as monitoring abnormal gait [3], [4]. The combination and correlation analyses of trunk and limb velocities are essential for evaluating effectiveness of physical rehabilitation [5] and improving running economy for marathon runners [6]. Based on deep-learning and genetic algorithms [7], human action recognition can be implemented
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
