Hanging: Commonly Encountered by Forensic Experts at Autopsy.

Though human lives have become easier and faster due to rapid twist in urbanization, industrialization and digitalization suicidal tendency among common people are often seen. Hanging is the commonly chosen method to do so. The study was designed to find out the pattern of hanging cases and to discover the immensity of hanging as a method of committing suicide. This retrospective study was done for three years by retrospectively collected data at the department of Forensic Medicine and Toxicology of Chattogram Medical College. A total of 193 cases (6.73%) of hanging were observed among 2850 autopsies done from January 2015 to December 2017. The age group of 31-40 years was mostly affected i.e. in 54(27.97%) followed by 21-30 years 48(24.87%). Males 112(58.03%) out numbered the females 81(41.96%). Clothes 88(45.59%) were mostly used as ligature material followed by jute rope (33.67%) and nylon rope (20.72%). Maximum cases were atypical hanging 160(82.90%), while we observed 33 typical (17.09%) hangings. In 166 cases (86.01%) we observed no injuries to the neck while contusion of the neck in 27 cases (13.99%). This study revealed fracture of the thyroid cartilage in 5 cases (2.59%) and hyoid in 2 cases (1.03%). One hundred & eighty five (185) cases (95.85%) were of suicidal and only 8 cases (4.14%) were of accidental. As per observation of this study, hanging has been found to be a common means of committing suicide in Bangladesh.

Comparative study and multi-parameter analysis to optimize device structure of triboelectric nanogenerators.

Triboelectric nanogenerator is becoming one of the most efficient energy harvesting device among all mechanical energy harvesters. This device consists of dielectric friction layers and metal electrode which generates electrical charges using electrostatic induction effect. There are several factors influencing the performance of this generator which needs to be evaluated prior to experiment. The absence of a universal technique for TENG simulation makes the device design and optimization hard before practical fabrication, which also lengthens the exploration and advancement cycle and hinders the arrival of practical applications. In order to deepen the understanding the core physic behind the working process of this device, this work will provide comparative analysis on different modes of TENG. Systematic investigation on different material combination, effect of material thickness, dielectric constant and impact of surface patterning is evaluated to shortlist the best material combination. COMSOL Multiphysics simulating environment is used to design, model and analyze factor affecting the overall output performance of TENG. The stationary study in this simulator is performed using 2D geometry structure with higher mesh density. During this study short circuit and open circuit condition were applied to observe the behavior of charge and electric potential produced. This observation is analyzed by plotting charge transfer/electric potential against various displacement distances of dielectric friction layers. The ouput is then provided to load ciruitary to measure the maximum output power of the models. Overall, this study provides an excellent understanding and multi-parameter analysis on basic theoretical and simulation modeling of TENG device.

Evaluating fundamental biochar properties in relation to water holding capacity.

Biochar products that hold and release water within a stable carbonised porous structure provide many opportunities for climate mitigation and a range of applications such as for soil amendments. Biochar that are produced from various organic feedstocks by pyrolysis can provide multiple co-benefits to soil including improving soil health and productivity, pH buffering, contaminant control, nutrient storage, and release, however, there are also risks for biochar application in soils. This study evaluated fundamental biochar properties that influence Water Holding Capacity (WHC) of biochar products and provides recommendations for testing and optimising biochar products prior to soil applications. A total of 21 biochar samples (locally sourced, commercially available, and standard biochars) were characterised for particle properties, salinity, pH and ash content, porosity, and surface area (with N2 as adsorbate), surface SEM imaging, and several water testing methods. Biochar products with mixed particle size, irregular shapes, and hydrophilic properties were able to rapidly store relatively large volumes of water (up to 400% wt.). In contrast, relatively less water (as low as 78% wt.) was taken up by small-sized biochar products with smooth surfaces, along with hydrophobic biochars that were identified by the water drop penetration test (rather than contact angle test). Water was stored mostly in interpore spaces (between biochar particles) although intra-pore spaces (meso-pore and micropore scale) were also significant for some biochars. The type of organic feedstock did not appear to directly affect water holding, although further work is needed to evaluate mesopore scale processes and pyrolytic conditions that could influence the biochemical and hydrological behaviour of biochar. Biochars with high salinity, and carbon structures that are not alkaline pose potential risks when used as soil amendments.

Advanced triboelectric nanogenerator-driven drug delivery systems for targeted therapies.

In the current decade, remarkable efforts have been made to develop a self-regulated, on-demand and controlled release drug delivery system driven by triboelectric nanogenerators (TENGs). TENGs have great potential to convert biomechanical energy into electricity and are suitable candidates for self-powered drug delivery systems (DDSs) with exciting features such as small size, easy fabrication, biocompatible, high power output and economical. This review exclusively explains the development and implementation process of TENG-mediated, self-regulated, on-demand and targeted DDSs. It also highlights the recently used TENG-driven DDSs for cancer therapy, infected wounds healing, tissue regeneration and many other chronic disorders. Moreover, it summarises the crucial challenges that are needed to be addressed for their universal applications. Finally, a roadmap to advance the TENG-based drug delivery system developments is depicted for the targeted therapies and personalised healthcare.

Optimising water holding capacity and hydrophobicity of biochar for soil amendment - A review.

Biochar is a product of the thermal treatment of biomass, and it can be used for enhancing soil health and productivity, soil carbon sequestration, absorbance of pollutants from water and soil, and promoting environmental sustainability. Extensive research has been done on applications of biochar to enhance the Water Holding Capacity (WHC) of biochar amended soil. However, a comprehensive road map of biochar optimised for enhanced WHC, and reduced hydrophobicity is not yet published. This review is the first to provide not only quantitative information on the impacts of biochar properties in WHC and hydrophobicity, but also a road map to optimise biochar for enhanced WHC when applied as a soil amendment. The review shows that straw or grass-derived biochar (at 500-600 °C) increases the WHC of soil if applied at 1 to 3 % in the soil. It is clear from the review that soil of varying texture requires different particle sizes of biochar to enhance the WHC and reduce hydrophobicity. Furthermore, the review concludes that ageing biochar for at least a year with enhanced oxidation is recommended for improving the WHC and reducing hydrophobicity compared to using biochar immediately after production. Additionally, while producing biochar a residence time of 1 to 2 h is recommended to reduce the biochar's hydrophobicity. Finally, a road map for optimising biochar is presented as a schematic that can be a resource for making decisions during biochar production for soil amendment.

Recent advancements in bioelectronic devices to interface with the peripheral vestibular system.

Balance disorders affect approximately 30% of the population throughout their lives and result in debilitating symptoms, such as spontaneous vertigo, nystagmus, and oscillopsia. The main cause of balance disorders is peripheral vestibular dysfunction, which may occur as a result of hair cell loss, neural dysfunction, or mechanical (and morphological) abnormality. The most common cause of vestibular dysfunction is arguably vestibular hair cell damage, which can result from an array of factors, such as ototoxicity, trauma, genetics, and ageing. One promising therapy is the vestibular prosthesis, which leverages the success of the cochlear implant, and endeavours to electrically integrate the primary vestibular afferents with the vestibular scene. Other translational approaches of interest include stem cell regeneration and gene therapies, which aim to restore or modify inner ear receptor function. However, both of these techniques are in their infancy and are currently undergoing further characterization and development in the laboratory, using animal models. Another promising translational avenue to treating vestibular hair cell dysfunction is the potential development of artificial biocompatible hair cell sensors, aiming to replicate functional hair cells and generate synthetic 'receptor potentials' for sensory coding of vestibular stimuli to the brain. Recently, artificial hair cell sensors have demonstrated significant promise, with improvements in their output, such as sensitivity and frequency selectivity. This article reviews the history and current state of bioelectronic devices to interface with the labyrinth, spanning the vestibular implant and artificial hair cell sensors.

A Smart Camera Trap for Detection of Endotherms and Ectotherms.

Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks.

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks.

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project's aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

Automatic Target Detection from Satellite Imagery Using Machine Learning.

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Design of a Highly Efficient Wideband Multi-Frequency Ambient RF Energy Harvester.

For low input radio frequency (RF) power from -35 to 5 dBm, a novel quad-band RF energy harvester (RFEH) with an improved impedance matching network (IMN) is proposed to overcome the poor conversion efficiency and limited RF power range of the ambient environment. In this research, an RF spectral survey was performed in the semi-urban region of Malaysia, and using these results, a multi-frequency highly sensitive RF energy harvester was designed to harvest energy from available frequency bands within the 0.8 GHz to 2.6 GHz frequency range. Firstly, a new IMN is implemented to improve the rectifying circuit's efficiency in ambient conditions. Secondly, a self-complementary log-periodic higher bandwidth antenna is proposed. Finally, the design and manufacture of the proposed RF harvester's prototype are carried out and tested to realize its output in the desired frequency bands. For an accumulative -15 dBm input RF power that is uniformly universal across the four radio frequency bands, the harvester's calculated dc rectification efficiency is about 35 percent and reaches 52 percent at -20 dBm. Measurement in an ambient RF setting shows that the proposed harvester is able to harvest dc energy at -20 dBm up to 0.678 V.

Disruptive technologies as a solution for disaster risk management: A review.

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Quad-Band Rectenna for Ambient Radio Frequency (RF) Energy Harvesting.

RF power is broadly available in both urban and semi-urban areas and thus exhibits as a promising candidate for ambient energy scavenging sources. In this research, a high-efficiency quad-band rectenna is designed for ambient RF wireless energy scavenging over the frequency range from 0.8 to 2.5 GHz. Firstly, the detailed characteristics (i.e., available frequency bands and associated power density levels) of the ambient RF power are studied and analyzed. The data (i.e., RF survey results) are then applied to aid the design of a new quad-band RF harvester. A newly designed impedance matching network (IMN) with an additional L-network in a third-branch of dual-port rectifier circuit is familiarized to increase the performance and RF-to-DC conversion efficiency of the harvester with comparatively very low input RF power density levels. A dual-polarized multi-frequency bow-tie antenna is designed, which has a wide bandwidth (BW) and is miniature in size. The dual cross planer structure internal triangular shape and co-axial feeding are used to decrease the size and enhance the antenna performance. Consequently, the suggested RF harvester is designed to cover all available frequency bands, including part of most mobile phone and wireless local area network (WLAN) bands in Malaysia, while the optimum resistance value for maximum dc rectification efficiency (up to 48%) is from 1 to 10 kΩ. The measurement result in the ambient environment (i.e., both indoor and outdoor) depicts that the new harvester is able to harvest dc voltage of 124.3 and 191.0 mV, respectively, which can be used for low power sensors and wireless applications.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation.

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

A Rare Case of Polyorchidism: Sonographic and MR Evaluation of Four Testes.

Polyorchidism is a rare congenital anomaly reported about 200 cases in the world text. A number of theories have been planned concerning the making of polyorchidism, but the real explanation is still not acknowledged. Here we are going to present a case study of polyorchidism. A 70 years old gentleman complained with left supernumerary testes in the left hemiscrotum. His left hemiscrotum was painless with mass. Polyorchidism without malignancy or any other concomitant features were revealed by both ultrasound and MRI examinations. In most cases the ultrasonograph alone is diagnostic. In complicated cases of polyorchidism MRI may provide additional information.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus.

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

Cluster Analysis and Model Comparison Using Smart Meter Data.

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

Electrothermal Modeling and Analysis of Polypyrrole-Coated Wearable E-Textiles.

The inhomogeneity of the resistance of conducting polypyrrole-coated nylon-Lycra and polyester (PET) fabrics and its effects on surface temperature were investigated through a systematic experimental and numerical work including the optimization of coating conditions to determine the lowest resistivity conductive fabrics and establish a correlation between the fabrication conditions and the efficiency and uniformity of Joule heating in conductive textiles. For this purpose, the effects of plasma pre-treatment and molar concentration analysis of the dopant anthraquinone sulfonic acid (AQSA), oxidant ferric chloride, and monomer pyrrole was carried out to establish the conditions to determine the sample with the lowest electrical resistance for generating heat and model the experiments using the finite element modeling (FEM). Both PET and nylon-Lycra underwent atmospheric plasma treatment to functionalize the fabric surface to improve the binding of the polymer and obtain coatings with reduced resistance. Both fabrics were compared in terms of average electrical resistance for both plasma treated and untreated samples. The plasma treatment induced deep black coatings with lower resistance. Then, heat-generating experiments were conducted on the polypyrrole (PPy) coated fabrics with the lowest resistance using a variable power supply to study the distribution and maximum value of the temperature. The joule heating model was developed to predict the heating of the conductive fabrics via finite element analysis. The model was based on the measured electrical resistance at different zones of the coated fabrics. It was shown that, when the fabric was backed with neoprene insulation, it would heat up quicker and more evenly. The average electrical resistance of the PPy-PET sample used was 190 Ω, and a maximum temperature reading of 43 °C was recorded. The model results exhibited good agreement with thermal camera data.

Advances in 4D-printed physiological monitoring sensors.

Physiological monitoring sensors have been critical in diagnosing and improving the healthcare industry over the past 30 years, despite various limitations regarding providing differences in signal outputs in response to the changes in the user's body. Four-dimensional (4D) printing has been established in less than a decade; therefore, it currently offers limited resources and knowledge. Still, the technique paves the way for novel platforms in today's ever-growing technologies. This innovative paradigm of 4D printing physiological monitoring sensors aspires to provide real-time and continuous diagnoses. In this perspective, we cover the advancements currently available in the 4D printing industry that has arisen in the last septennium, focusing on the overview of 4D printing, its history, and both wearable and implantable physiological sensing solutions. Finally, we explore the current challenges faced in this field, translational research, and its future prospects. All of these aims highlight key areas of attention that can be applied by future researchers to fully transform 4D printed physiological monitoring sensors into more viable medical products.