With the miniaturization of medical equipment and the comfort of diagnosis mode, positioning has become a major challenge in the research of capsule robots. In order to realize the effective positioning of magnetically controlled capsule robots, a positioning method based on double-layer symmetric sensor array is proposed in this work. Firstly, a near-field error correction method based on multi-magnetic dipole model is presented to improve the accuracy of capsule robot magnetic field model. Then, for multi-magnetic driving environment, an arrangement of double-layer symmetric sensor array is designed to reduce the interference of hybrid magnetic fields. The effectiveness of this arrangement is verified by calculating the elimination error of magnetic field in symmetric planes. Driving and positioning device are developed, and a BP neural network model is trained by recording the specific position of the capsule robot and the corresponding magnetic field. The trained BP neural network
In this work, we present a systematic review on non-invasive HMIs employing hybrid wearable sensor modalities for recognition of upper limb intentions. Different combinations of the sensors are investigated. As sEMG is dominant in the applications of externally powered prosthetic hands, it is involved in most hybrid sensor combinations. The combined use of sEMG and IMU is most studied in the literature as IMU is easy to be integrated. Though limited, the investigation on other hybrid modalities has been drawing more and more research attention and efforts, especially those with FMG and NIRS. For all the reported hybrid sensors, it is verified that this strategy can enrich the information of user intention and help the pattern recognition and/or intensity regulation of robotic hand/arm prosthesis. Though it is the trend, the development of these hybrid-sensor-based HMIs are still at the preliminary stage. More dedicated sensor fusion models and system architectures as well as new hybrid