As a matter of fact, the accelerometer detects total acceleration of gravity and motion accelerations 26, 27 that precludes it from determining motion velocity or attitude angle independently, and gyroscope based attitude estimation suffers from integral drift error. Among these sensors, body-worn inertial sensors including micro accelerometers and micro gyroscopes are the most commonly used wearable movement sensors 23, 24 due to their capability of direct measurement on body segment movement, which is important for not only quantitative assessment of motor function 9, but also interaction and control of rehabilitation robots and prostheses 25.Īlthough the popular uses of inertial sensors in motion capture, technique challenges still exist in detecting dynamic motion of human limbs. The wearable sensors provide promising tools for the next generation rehabilitation exoskeletons, such as soft exosuits, in which lightweight and comfort are concerned 17, 22. Wearable motion sensors, such as force based sensors 11, 12, 13, surface electromyography sensors 14, 15, 16, soft strain sensors 17, 18, 19, 20, and micro inertial sensors 21, may overcome such problems. But its noncompliance with human joint that has multi-degree-of-freedom disturbs the natural pattern of human motion and leads to discomfort and even joint injury in long-term applications 9, 10. Goniometer (or angle encoder) is another commonly-used motion capture device applied in rehabilitation robotics. However, high cost, complex setup and susceptibility to lighting condition and occlusion limit their applications only for laboratory 7, 8. Optical system (or machine vision system) is one of the most popular solutions for motion capture. It allows machine to assist users and improve life quality in such as senior care, physical rehabilitation, daily life-logging, personal fitness, and assistance for people with cognitive disorders and motor dysfunctions 1, 2, 3, 4, 5, 6. Motion capture technology plays an essential role in action recognition, motor function assessment and dexterous human-robot interaction for rehabilitation robots and intelligent prosthetics. 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. 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. 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. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises.
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.
Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots.