There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived.

Recently, flexible robotic systems are developed to enhance minimally invasive interventions on internal organs located in confined areas of human body. These surgical devices are designed to navigate anatomical pathways via single-port access, such as natural orifices or minimal incisions and intraluminal interventions. With improved precision, spatial flexibility and dexterity, the robotic technology can enhance surgery such that minimally invasive flexible access would become a faster, safer, and more convenient method for intra-body interventions without multiple or wide incisions. However, a lot of works are still required for global acceptance of existing flexible robotic surgical platforms. This review provides extended insights on the design details of two types of flexible robotic systems used for endoscopic and endovascular procedures. As of today, several prototypes of both platforms have been proposed; however, their global acceptability and applicability remains very low. To address these, we present an extensive review on design constraints and control methods which are vital for safer, faster, and better operation of the flexible robotic systems in minimally invasive surgery (MIS). Finally, research trends of flexible robotic systems and their clinical application status in MIS are discussed along with some of the technical and technological challenges hindering their prominence.

Soil respiration (R_s) represents the largest flux of CO₂ from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R_s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R_s, we showed that land-cover change played a more important role in regulating R_s changes in temperate and boreal regions during 2000–2014. Significant changes in R_s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R_s patterns and highlight the importance of land-cover change in driving global and regional R_s changes.

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

With the increasing demand for smart wearable clothing, the textile piezoelectric pressure sensor (T-PEPS) that can harvest mechanical energy directly has attracted significant attention. However, the current challenge of T-PEPS lies in remaining the outstanding output performance without compromising its wearing comfort. Here, a novel structural hierarchy T-PEPS based on the single-crystalline ZnO nanorods are designed. The T-PEPS is constructed with three layers mode consisting of a polyvinylidene fluoride (PVDF) membrane, the top and bottom layers of conductive rGO polyester (PET) fabrics with self-orientation ZnO nanorods. As a result, the as-fabricated T-PEPS shows low detection limit up to 8.71 Pa, high output voltage to 11.47 V and superior mechanical stability. The sensitivity of the sensor is 0.62 V·kPa⁻¹ in the pressure range of 0–2.25 kPa. Meanwhile, the T-PEPS is employed to detect human movements such as bending/relaxation motion of the wrist, bending/stretching motion of each finger. It is demonstrated that the T-PEPS can be up-scaled to promote the application of wearable sensor platforms and self-powered devices.

Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R² = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.