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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.

This paper aims to investigate an autonomous obstacle-surmounting method based on a hybrid gait for the problem of crossing low-height obstacles autonomously by a six wheel-legged robot. The autonomy of obstacle-surmounting is reflected in obstacle recognition based on multi-frame point cloud fusion.

In this paper, first, for the problem that the lidar on the robot cannot scan the point cloud of low-height obstacles, the lidar is driven to rotate by a 2D turntable to obtain the point cloud of low-height obstacles under the robot. Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping algorithm, fast ground segmentation algorithm and Euclidean clustering algorithm are used to recognize the point cloud of low-height obstacles and obtain low-height obstacle in-formation. Then, combined with the structural characteristics of the robot, the obstacle-surmounting action planning is carried out for two types of obstacle scenes. A segmented approach is used for action planning. Gait units are designed to describe each segment of the action. A gait matrix is used to describe the overall action. The paper also analyzes the stability and surmounting capability of the robot’s key pose and determines the robot’s surmounting capability and the value scheme of the surmounting control variables.

The experimental verification is carried out on the robot laboratory platform (BIT-6NAZA). The obstacle recognition method can accurately detect low-height obstacles. The robot can maintain a smooth posture to cross low-height obstacles, which verifies the feasibility of the adaptive obstacle-surmounting method.

The study can provide the theory and engineering foundation for the environmental perception of the unmanned platform. It provides environmental information to support follow-up work, for example, on the planning of obstacles and obstacles.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Stainless-steel electromagnetic shielding (EMS) fabrics are widely applied as protective materials against electromagnetic interference (EMI). However, these fabrics primarily shield electromagnetic waves through reflection, which can lead to the formation of resonance effects that severely compromise their protective capabilities and potentially cause secondary electromagnetic pollution in the external environment.

In this paper, carbon nanotube fibers are added via spacing method to replace some stainless-steel fibers to impart absorbing properties to stainless-steel EMS fabric. The shielding effectiveness (SE) of the EMS fabrics across various polarization directions is analyzed. Additionally, a spacing arrangement for the carbon nanotube fibers is designed. The EMS fabric with carbon nanotube fibers is manufactured using a semi-automatic sample loom, and its SE is tested using a small window method test box in both vertical and horizontal polarization directions.

According to the experimental data and electromagnetic theory analysis, it is determined that when the spacing between the carbon nanotube fibers is less than a specific distance, the SE of the stainless-steel EMS fabric significantly improves. The fabric exhibits stable absorbing properties within the tested frequency range, effectively addressing the issue of secondary damage that arises from relying solely on reflective shielding. Conversely, as the spacing between the carbon nanotube fibers exceeds this distance, the SE diminishes. Notably, the SE in the vertical polarization direction is substantially higher than that in the horizontal polarization direction at the same frequency.

This study provides a new path for the development of high-performance EMS fabrics with good wave-absorption characteristics and SE.

One of the challenges encountered in the design of guided projectiles is their prohibitive cost. To diminish it, an appropriate avenue many researchers have explored is the use of the non-actuator method for guiding the projectile to the target. In this method, biologically inspired by the flying concept of the single-winged seed, for instance, that of maple and ash trees, the projectile undergoes a helical motion to scan the region and meet the target in the descent phase. Indeed, the projectile is a decelerator device based on the autorotation flight while it attempts to resemble the seed’s motion using two wings of different spans. There exists a wealth of studies on the stability of the decelerators (e.g. the mono-wing, samara and pararotor), but all of them have assumed the body (exclusive of the wing) to be symmetric and paid no particular attention to the scanning quality of the region. In practice, however, the non-actuator-guided projectiles are asymmetric owing to the presence of detection sensors. This paper aims to present an analytical solution for stability analysis of asymmetric decelerators and apprise the effects of design parameters to improve the scanning quality.

The approach of this study is to develop a theoretical model consisting of Euler equations and apply a set of non-dimensionalized equations to reduce the number of involved parameters. The obtained governing equations are readily applicable to other decelerator devices, such as the mono-wing, samara and pararotor.

The results show that the stability of the body can be preserved under certain conditions. Moreover, pertinent conclusions are outlined on the sensitivity of flight behavior to the variation of design parameters.

The analytical solution and sensitivity analysis presented here can efficiently reduce the design cost of the asymmetric decelerator.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.