In August 2023, Xiaomi unveiled the Redmi K60 Ultra, the first multi-frequency smartphone integrated with BeiDou-3 Navigation Satellite System Precise Point Positioning (PPP-B2b) services and employing PPP technology as the primary positioning method. The positioning enhancement service is provided by the Assisted Global Navigation Satellite System (A-GNSS) location platform developed by the China Academy of Information and Communications Technology. The signaling interaction between the server and the users strictly adheres to the Third Generation of Mobile Communications Technology Partnership Project Long-Term Evolution Positioning Protocol and the Open Mobile Alliance Secure User Plane Location framework. To comprehensively evaluate the Redmi K60 Ultra’s capabilities, this study designed six distinct experimental scenarios and conducted comprehensive research on multi-frequency and multi-GNSS observation noise, Time to First Fix (TTFF), as well as the performance of both GNSS-based and network-based positioning. Experimental results indicate that the GNSS chipset within the Redmi K60 Ultra has achieved a leading position in the consumer market concerning supported satellite constellations, frequencies, and observation accuracy, and is comparable to some low-cost GNSS receivers. A-GNSS positioning can reduce the TTFF from 30 to under 5 s, representing an improvement of over 85% in the cold start speed compared to a standalone GNSS mode. The positioning results show that the A-GNSS PPP-B2b service can achieve positioning performance with RMS errors of less than 1.5 m, 2.5 m, and 4 m in open-sky, realistic, and challenging urban environments. Compared to GNSS-based positioning, cellular network-based Observed Time Difference of Arrival (OTDOA) positioning achieves an accuracy ranging from tens to hundreds of meters in various experimental scenarios and currently functions primarily as coarse location determination. Additionally, this study explores the potential of the Three-Dimensional Mapping-Aided (3DMA) GNSS algorithm in detecting Non-Line-of-Sight signals and enhancing positioning performance. The results indicate that 3DMA PPP, as compared to conventional PPP, can significantly accelerate PPP convergence and improve positioning accuracy by over 30%. Consequently, 3D city models can be utilized as future assistance data for the A-GNSS location platform.

Objective: Wireless capsule endoscopy has been well used for gastrointestinal (GI) tract diagnosis. However, it can only obtain images and cannot take samples of GI tract tissues. In this study, we designed a magnetically-actuated biopsy capsule (MABC) robot for GI tract diagnosis. Methods: The proposed robot can achieve locomotion and biopsy functions under the control of external electromagnetic actuation (EMA) system. Two types of active locomotion can be achieved, plane motion refers to the robot rolling on the surface of the GI tract with a rotating uniform magnetic field. 3D motion refers to the robot moving in 3D space under the control of the EMA system. After reaching the target position, the biopsy needle can be sprung out for sampling and then retracted under a gradient magnetic field. Results: A pill-shaped robot prototype ( $\phi$ 15 mm×32 mm) has been fabricated and tested with phantom experiments. The average motion control error is 0.32 mm in vertical direction, 3.3 mm in horizontal direction, and the maximum sampling error is about 5.0 mm. The average volume of the sampled tissue is about 0.35 mm $^{3}$ . Conclusion: We designed a MABC robot and proposed a control framework which enables planar and 3D spatial locomotion and biopsy sampling. Significance: The untethered MABC robot can be remotely controlled to achieve accurate sampling in multiple directions without internal power sources, paving the way towards precision sampling techniques for GI diseases in clinical procedures.

Foot-mounted inertial pedestrian positioning system (FIPPS) is increasingly important in the Internet of Things (IoT) and smart cities because of the passive, anti-interference, and fully automatic advantages. However, FIPPS faces the problem of unknown initial positions and accumulative errors in practical applications, which makes the positioning trajectory inaccurate and unable to be matched in a well-defined coordinate system. A seamless indoor–outdoor inertial pedestrian positioning method based on smartphone precise point positioning (PPP)/3-D map/barometer augmentation is proposed to solve this problem. The main contributions include the following: 1) a 3-D-mapping-aided PPP (3DMA PPP) method is proposed to detect and eliminate non-line-of-sight (NLOS) signals to provide high-precision initial coordinates for FIPPS, which enables positioning trajectories to be matched in the unified WGS-84 coordinate system; 2) the adaptive zero-velocity update (ZUPT) approach based on neuro-fuzzy inference is used to precisely identify the stance phases of various gait patterns to suppress position and velocity error accumulation; 3) a barometer and 3-D building model-based height constraint algorithm is applied to further improve the height estimation of the FIPPS; and 4) an Android application named FYTECH is developed for data transmission between the smartphone and FIPPS and supports the online display of pedestrian positioning trajectories. In a seamless outdoor–indoor experiment with multiple motion patterns, including walking, running, elevators and stairs, we demonstrate that the proposed method can achieve RMS errors better than 1.17 and 1.32 m in the horizontal and vertical directions, respectively. The performance indicates that our method can conveniently fuse multisource sensor information, such as foot wearables, smartphones, and 3-D maps to greatly enhance pedestrians’ seamless indoor–outdoor navigation experience.

In this paper, we propose a gait recognition method for service robots to conduct human following tasks. A walking sequence segmentation method is designed to extract the consecutive gait cycles from an arbitrary walking sequence. Based on the segmentation results, a novel hybrid gait feature is proposed to capture the static, dynamic, and trajectory features for each segmented key and supplementary gait cycles. A dataset of 25 human subjects is collected to evaluate the proposed method in three different walking paths with various walking directions. Experimental results show that the proposed method achieves satisfactory performance in terms of identification accuracy and Fcomb indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.

Magnetic localization methods have been widely studied to provide accurate position and orientation information of intra-body objects, such as wireless capsule endoscopes. However, these methods cannot work well in dynamic scenarios when using a wearable sensor array due to the geomagnetic field. In this article, we propose a novel approach for tracking wireless capsules in a mobile setup based on differential signals from adjacent magnetic sensors. Compared to the previous passive magnetic localization framework, the proposed algorithm can eliminate the interference of the geomagnetic field without introducing other compensation sensors or signals. As a result, the proposed method can be used for wearable sensor arrays without knowing the distribution of the geomagnetic field and the movement of the sensor array in advance. Moreover, the relationship between position error and distance of adjacent sensors has been studied to improve tracking accuracy. Finally, a sensor array with 16 three-axis magnetic sensors is developed. Both static and dynamic experiments have been carried out, whose results verified the effectiveness of the proposed method.

This paper presents a sensor fusion-based surgical instrument tracking system which uses multiple monocular modules. The system is an optical tracking system, which has been widely utilized in the image-guide surgery because of its high accuracy and precision. However, the line-of-sight occlusion problem which remains unresolved in current systems frustrates surgeons during the operation. To address this challenge, we propose a surgical instrument tracking system based on multiple monocular modules. The rationale is to enable the system to track the surgical instruments inside the surgical site from different views. Three sensor fusion algorithms are proposed to integrate all sensor data from the multimodule system. In order to show the feasibility of the tracking system, simulations and comparison experiments have been carried out. The intensive investigation results give a practical instruction to the real implementation of the proposed system in image-guided interventions. Moreover, an image-guided surgical trial by using a cadaver head has been carried out to validate the feasibility of the proposed system and the tracking algorithms. The results from both the simulation and the cadaver trial have shown the effectiveness of the proposed robust fusion algorithm.