A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to tes...A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to test and verify the gesture operation. Following that, a real time operating wheelchair was developed consisting of mode changing option between joystick control mode and head gesture control mode as per as the user’s requirement. The wheelchair consists of MPU6050 sensor, joystick module, RF module, battery, dc motor, toggle switch and Arduino. The movement of the head is detected by MPU6050 and the signal is transmitted to the microcontroller. Then the signal is processed by controller and motion of wheelchair is enabled for navigation. The wheelchair was capable of moving left, right, forward and backward direction. The speed of the wheelchair was 4.8 km/h when tested. Design objective of the wheelchair included cost effectiveness without compromising safety, flexibility and mobility for the users.展开更多
Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in t...Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in this paper. A distributed parallelly decision fusion algorithm fused classification results of the two signals to form a final judgment. After sEMG was decomposed by wavelet packet, feature information of some subspace was weaken, because subspace dimension was very large. To solve the problem, the paper proposed an improved wavelet packet decomposition algorithm, which extracted sample entropy from four subspaces of improved wavelet packet decomposition and took it as the feature information. Experimental results show that the intelligent wheelchair system based on sEMG and head gesture has not only a simple operation and shorter operating time, but also a better stability and security.展开更多
文摘A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to test and verify the gesture operation. Following that, a real time operating wheelchair was developed consisting of mode changing option between joystick control mode and head gesture control mode as per as the user’s requirement. The wheelchair consists of MPU6050 sensor, joystick module, RF module, battery, dc motor, toggle switch and Arduino. The movement of the head is detected by MPU6050 and the signal is transmitted to the microcontroller. Then the signal is processed by controller and motion of wheelchair is enabled for navigation. The wheelchair was capable of moving left, right, forward and backward direction. The speed of the wheelchair was 4.8 km/h when tested. Design objective of the wheelchair included cost effectiveness without compromising safety, flexibility and mobility for the users.
基金supported by the International Cooperation Project of Ministry of Science and Technology (2010DFA12160)
文摘Because the single channel surface electromyographic （sEMG） signals easily caused a complex operation during the real-time operation, an intelligent wheelchair system based on sEMG and head gesture was proposed in this paper. A distributed parallelly decision fusion algorithm fused classification results of the two signals to form a final judgment. After sEMG was decomposed by wavelet packet, feature information of some subspace was weaken, because subspace dimension was very large. To solve the problem, the paper proposed an improved wavelet packet decomposition algorithm, which extracted sample entropy from four subspaces of improved wavelet packet decomposition and took it as the feature information. Experimental results show that the intelligent wheelchair system based on sEMG and head gesture has not only a simple operation and shorter operating time, but also a better stability and security.
