Abstract
Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.
Highlights
Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots
To determine attitude angles with robust performance of antiinterference, we propose a tailor-designed filter algorithm considering natural dynamics and inherent correlation between motion velocity and acceleration to implement data fusion of the motion velocity detected by the flow sensor and inertial quantities detected by the accelerometer and gyroscope
Above experimental results demonstrate that the proposed approach using the wearable device are accurate, reliable and robust for dynamic motion capture of human limbs even in strenuous exercises like boxing and kicking
Summary
Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. In capturing high dynamic motion of human limbs, the attitude angles are mainly estimated by integral of gyroscope output rather than gravity acceleration via accelerometer due to highly dynamic interference, and suffers from drift problem. Great efforts have been made in data fusion algorithm for inertial sensors[26,34,35,36,37,38,39], inherent problems of drift and instability in longterm monitoring of highly dynamic limb motions still exist[40], for example limb posture capture in running. We achieve accurate and robust limb motion capture in highly dynamic activities of human body using a simple wearable device
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