Aiming at solving the problem of joint angle measurement during the movement of small robots or robotic arms that cannot be equipped with angle encoders, this paper provides a small, lightweight and detachable measurement system. Compared with traditional magnetic and optical joint angle sensors, the principle of this method has the advantages of flexible installation and non-invasive measurement. By simply binding two inertial measurement units (IMUs) on both sides of the robot joint in any pose, real-time and high-precision robot joint angle measurement can be achieved through wireless transmission. At the same time, the relationship between the IMU and the joint pose changes dynamically with the robot, which can avoid measurement errors due to loose binding or robot vibration. In this paper, experiments were carried out on a knee joint rehabilitation training robot which can realize two rehabilitation actions, and the precise and stable joint angle curve of the robot is obtained.
This paper presents a lower limb motion pattern recognition algorithm based on SVM model optimized by improved whale algorithm (IWOA-SVM). The purpose is to overcome the problems of low accuracy and low reaction speed of existing lower limb rehabilitation pattern recognition algorithms, nonlinear adjustment factor is combined with WOA to enhance the search ability and search speed of the algorithm. The collected surface EMG signals are used as the input of the motion pattern recognition system, and the motion pattern recognition is realized by combining the IWOA-SVM model. Simulation experiments on six test functions are carried out and compared with WOA. The results indicated that the search convergence speed and optimization accuracy of IWOA are improved. Experiments on the collected signal data suggested that the recognition accuracy is 94.12%, compared with WOA-SVM algorithm, PSO-SVM and GA-SVM. The results indicated that the recognition accuracy of this algorithm is improved by 4.02%, 8.82% and 6.64% respectively.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.