The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

This paper introduces a novel 6 degrees of freedom (6-D) spatial localization for wireless magnetically actuated capsule endoscopes (WMACEs) based on multi-sensor fusion. A WMACE integrated with an inertial measurement unit (IMU) sensor is developed, which enables wireless transmission of video and sensor data. A Hall sensors array is designed to measure the magnetic field generated by the WMACE for acquiring its 6-D pose under external magnetic actuation. The angle between the direction of the WMACE’s magnetic moment and the gravity direction measured by the IMU is set initially as 90°, which, based on multi-sensor data fusion, solves the problem that the rotation around the magnetization axis cannot be acquired by Hall sensors array and that the rotation around the gravity axis cannot be measured by the IMU. In addition, eight pose detection algorithms and two data-fusion filters are experimentally compared for analyzing and selecting the optimal approach for the 6-D localization of the WMACE. The experimental results indicate a high accuracy for 6-D pose detection, with a mean absolute error of 1.463 mm for 3-D position and 0.419° for 3-D orientation, with the height between the capsule and the Hall sensor array ranging from 25 mm to 72 mm.

Inductive coupling, a form of wireless power transfer (WPT) offers a promising solution for enabling wireless capsule endoscopes (WCEs) when direct wire connections are impractical. In this approach, a 1-D receiving coil is integrated into a WCE, which is positioned within the stomach. Simultaneously, a 3-D transmitting coil (3DTC) is constructed outside the human body to provide the necessary power for the operation of the WCE. The WPT system described in this article is the first complete prototype wherein the receiving coil is integrated within a fully functional WCE and capable of accommodating an adult human body with the 3DTC. Mathematical analysis and computer simulations have verified the magnetic field direction at the center of the 3DTC can be precisely controlled to any orientation by manipulating the 3DTC currents. The static measurements of the WCE-received power at various angles from 0 to 180° range between 1210 mW and 1822.5 mW, which are all above the power requirement of typical WCEs. In the subsequent adaptive test, the inertial measurement unit in the WCE collects the orientation information of the capsule and, then, wirelessly transmits it to the microcontroller unit of the 3DTC controlling the coils' excitation. It is confirmed that the proposed system is able to control the coil excitation following the capsule rotation as it would within the human stomach.

Wireless power transfer (WPT) has become a crucial feature in numerous electronic devices, electric appliances, and electric vehicles. However, traditional design methods for WPT suffer from numerous drawbacks, such as time-consuming computations and high error counts due to inaccurate model parameters. As artificial intelligence (AI) continues to gain traction across industries, its ability to provide quick decisions and solutions makes it highly attractive for system optimizations. In this paper, a method for optimizing WPT parameters based on machine learning is proposed. The convolutional neural network is adapted for training and predicting the performance of a pair of coupled coils under a set of input parameters. The performance parameters include the spatial magnetic field distribution map, quality factor, inductance value, and mutual inductance value, which are critical in determining the efficiency and selecting optimal coil parameters such as the number of turns and wire diameter. Moreover, the spatial magnetic field distribution map is also helpful for identifying design compliance with the electromagnetic field safety standards. The training results reveal that the proposed method takes an average of 3.2 ms with a normalized image prediction error of 0.0034 to calculate the results to calculate one set of parameters, compared to an average of 23.74 s via COMSOL. This represents significant computational time savings while still maintaining acceptable computational accuracy.

In this article, a new attempt of utilizing pulse frequency modulation (PFM) control method to transfer power wirelessly to a pill-camera is presented. Experimental results confirm that the PFM control method surpassed the conventional phase shift control (PSC) method in terms of power loss reduction in both power converters and magnetic couplers. A 33.3% reduction in power loss and 55% gain in the overall system efficiency are recorded at maximum transmission power in a full-scale and fully functional engineering prototype. This significant improvement paves the way for the future development of wireless charging systems for humans, particularly when considering battery life. The target devices cover various health sensors, medical devices, biology, and industry applications. This article provides a complete study of proposed PFM method and comparison with conventional PSC method. The design parameters and fabrication of the engineering prototype are presented including a three-dimensional transmitting coil (3DTC) system and a one-dimensional receiving coil. The attitude information of the pill-camera is wirelessly transmitted to the controller to manipulate the magnitude and phase angle of the transmitter currents. System performance with the pill-camera in different orientations in 3DTC is measured. The advantage of using proposed control method is experimentally demonstrated and confirmed.