Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.
Human gait analysis aims to assess gait mechanics and to identify the deviations from "normal" gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 - 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (â¼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.
Gait , Walking , Humans , Reproducibility of Results , Gait Analysis , Biomechanical Phenomena
An aerosol jet printing (AJP) process for depositing ruthenium dioxide (RuO2) as a promising material for pH sensing is reported. Graphene oxide (GO) with a large surface area was used for the in situ sol-gel deposition of RuO2 nanoparticles on its surface. The cosolvent ratio and solid loading of the solution are adjusted to form a printable and stable ink. The monodispersed aerosol was atomized on the surface of the screen-printed carbon electrode in order to develop an integrated pH sensor. The RuO2-GO pH sensor demonstrates excellent performance, with a rapid response time of less than 5 s and high sensitivity in the pH range of 4-10. Compared to traditional carbon electrodes, the RuO2-GO sensor shows up to four times higher sensitivity. The increased sensitivity is a result of the consistent attachment of small-crystallized RuO2 nanoparticles onto the surface of GO sheets, leading to a synergistic effect. Thanks to the AJP method as a facile and cost-effective integration technique, the fabricated electrodes can serve as an alternative to traditional rigid pH electrodes for accurate pH measurement.
A novel, polarization-independent, wide-angle reception Chand-Bali nano-antenna is proposed. An adjoint-based optimization algorithm is used to create the same resonance at both linear polarizations of the incident radiation. The nano-antenna optimal parameters reveal that two hot spots with a strong field enhancement are created. These hot-spots could be integrated with metal-insulator-metal (MIM) diodes to form a rectenna for infrared (IR) energy harvesting. The metallic resonators allow for selecting several materials to facilitate the fabrication of the nano-antenna and the MIM diode. The Chand-Bali-based IR rectennas are investigated and simulations demonstrate an improvement of more than one order of magnitude in efficiency compared to ones using traditional nano-antennas.
Context awareness is a field in pervasive computing, which has begun to impact medical systems via an increasing number of healthcare applications that are starting to use context awareness. The present work seeks to determine which contexts are important for medical applications and which domains of context-aware applications exist in healthcare. A systematic scoping review of context-aware medical systems currently used by patients or healthcare providers (inclusion criteria) was conducted between April 2021 and June 2023. A search strategy was designed and applied to Pub Med, EBSCO, IEEE Explore, Wiley, Science Direct, Springer Link, and ACM, articles from the databases were then filtered based on their abstract, and relevant articles were screened using a questionnaire applied to their full texts prior to data extraction. Applications were grouped into context-aware healthcare application domains based on past reviews and screening results. A total of 25 articles passed all screening levels and underwent data extraction. The most common contexts used were user location (8 out of 25 studies), demographic information (6 out of 25 studies), movement status/activity level (7 out of 25 studies), time of day (5 out of 25 studies), phone usage patterns (5 out of 25 studies), lab/vitals (7 out of 25 studies), and patient history data (8 out of 23 studies). Through a systematic review process, the current study determined the key contexts within context-aware healthcare applications that have reached healthcare providers and patients. The present work has illuminated many of the early successful context-aware healthcare applications. Additionally, the primary contexts leveraged by these systems have been identified, allowing future systems to focus on prioritizing the integration of these key contexts.
Delivery of Health Care , Health Personnel , Humans
With the growing importance of single-photon-counting (SPC) techniques, researchers are now designing high-performance systems based on single-photon avalanche diodes (SPADs). SPADs with high performances and low cost allow the popularity of SPC-based systems for medical and industrial applications. However, few efforts were put into the design optimization of SPADs due to limited calibrated models of the SPAD itself and its related circuits. This paper provides a perspective on improving SPAD-based system design by reviewing the development of SPAD models. First, important SPAD principles such as photon detection probability (PDP), dark count rate (DCR), afterpulsing probability (AP), and timing jitter (TJ) are discussed. Then a comprehensive discussion of various SPAD models focusing on each of the parameters is provided. Finally, important research challenges regarding the development of more advanced SPAD models are summarized, followed by the outlook for the future development of SPAD models and emerging SPAD modeling methods.
Time-to-digital converters (TDCs) are high-performance mixed-signal circuits capable of timestamping events with sub-gate delay resolution. As a result of their high-performance, in recent years TDCs were integrated in complementary metal-oxide-semiconductor (CMOS) technology with highly sensitive photodetectors known as single-photon avalanche diodes (SPADs), to form digital silicon photomultipliers (dSiPMs) and SPAD imagers. Time-resolved SPAD-based sensors are capable of detecting the absorption of a single photon and timestamping it with picosecond resolution. As such, SPAD-based sensors are very useful in the field of biomedical imaging, using time-of-flight (ToF) information to produce data that can be used to reconstruct high-quality biological images. Additionally, the capability of integration in standard CMOS technologies, allows SPAD-based sensors to provide high-performance, while maintaining low cost. In this paper, we present an overview of fundamental TDC principles, and an analysis of state-of-the-art TDCs. Furthermore, the integration of TDCs into dSiPMs and SPAD imagers will be discussed, with an analysis of the current results of TDCs in different biomedical imaging applications. Finally, several important research challenges for TDCs in biomedical imaging applications are presented.
Photons , Semiconductors , Humans , Oxides , Technology
Cyber-physical-social systems (CPSS), an emerging cross-disciplinary research area, combines cyber-physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
Coherent detection provides the optimum performance for free space optical (FSO) communication systems. However, such detection systems are expensive and require digital phase noise compensation. In this paper, the transmission performance of long-haul FSO system for ground-to-satellite communication based on a Kramers-Kronig (KK) transceiver is evaluated. KK transceivers utilize inexpensive direct detection receivers and the signal phase is retrieved from the received current using the well-known KK relations. KK transceivers are not sensitive to the laser phase noise and, hence, inexpensive lasers with large linewidths can be used at the transmitter. The transmission performance of coherent and KK transceivers is compared in various scenarios such as satellite-to-ground, satellite-to-satellite, and ground-to-satellite for weak, moderate, and strong turbulence. The results show that the transmission performance of a system based on the KK transceiver is comparable to that based on a coherent transceiver, but at a significantly lower system cost and complexity. It is shown that in the absence of turbulence, the coherent receiver has a ~3 dB performance advantage over the KK receiver. However, in the presence of strong turbulence, this performance advantage becomes negligible.
Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals' daily activities, providing information that is reflective of an individual's health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual's health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual's health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.
Shoes , Wearable Electronic Devices , Foot , Gait , Humans , Pressure
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.
COVID-19 , Wearable Electronic Devices , Artificial Intelligence , Chronic Disease , Humans , Oxygen Saturation , SARS-CoV-2
Food wastage is a critical and world-wide issue resulting from an excess of food supply, poor food storage, poor marketing, and unstable markets. Since food quality depends on consumer standards, it becomes necessary to monitor the quality to ensure it meets those standards. Embedding sensors with active nanomaterials in food packaging enables customers to monitor the quality of their food in real-time. Though there are many different sensors that can monitor food quality and safety, pH sensors and time-temperature indicators (TTIs) are the most critical metrics in indicating quality. This review showcases some of the recent progress, their importance, preconditions, and the various future needs of pH sensors and TTIs in food packaging for smart sensors in food packaging applications. In discussing these topics, this review includes the materials used to make these sensors, which vary from polymers, metals, metal-oxides, carbon-based materials; and their modes of fabrication, ranging from thin or thick film deposition methods, solution-based chemistry, and electrodeposition. By discussing the use of these materials, novel fabrication process, and problems for the two sensors, this review offers solutions to a brighter future for the use of nanomaterials for pH indicator and TTIs in food packaging applications.
Food Packaging , Nanostructures , Food , Polymers , Temperature
The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endangered the health of many people around the world. COVID-19, which infects the lungs, is often diagnosed and managed using X-ray or computed tomography (CT) images. For such images, rapid and accurate classification and diagnosis can be performed using deep learning methods that are trained using existing neural network models. However, at present, there is no standardized method or uniform evaluation metric for image classification, which makes it difficult to compare the strengths and weaknesses of different neural network models. This paper used eleven well-known convolutional neural networks, including VGG-16, ResNet-18, ResNet-50, DenseNet-121, DenseNet-169, Inception-v3, Inception-v4, SqueezeNet, MobileNet, ShuffeNet, and EfficientNet-b0, to classify and distinguish COVID-19 and non-COVID-19 lung images. These eleven models were applied to different batch sizes and epoch cases, and their overall performance was compared and discussed. The results of this study can provide decision support in guiding research on processing and analyzing small medical datasets to understand which model choices can yield better outcomes in lung image classification, diagnosis, disease management and patient care.
COVID-19 , Deep Learning , Humans , Lung/diagnostic imaging , Neural Networks, Computer , RNA, Viral , SARS-CoV-2
BACKGROUND: Thousands of Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals Persistent follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis. METHODS: This study involves 462 laboratory-confirmed patients with COVID-19 who were admitted to Shenzhen Third People's Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 287 patients were followed up from 90 to 150 days after the onset of the disease, and lung function tests were conducted about three months after the onset. The risk factors affecting the persistence of pulmonary fibrosis were identified through regression analysis and the prediction model of the persistence of pulmonary fibrosis was established. RESULTS: Parenchymal bands, irregular interfaces, reticulation and traction bronchiectasis were the most common CT features in all COVID-19 patients. During the 0-30, 31-60, 61-90, 91-120 and > 120 days after onset, 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. More than half of the COVID-19 patients revealed abnormal conditions in lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups. CONCLUSIONS: Persistent pulmonary fibrosis was more likely to develop in patients with older age, higher BMI, severe/critical condition, fever, a longer viral clearance time, pre-existing disease and delayed hospitalization. Fibrosis developed in COVID-19 patients could be reversed in about a third of the patients after 120 days from onset. The pulmonary function of less than half of COVID-19 patients could turn to normal condition after three months from onset. An effective prediction model with an average area under the curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.
COVID-19/virology , Lung/virology , Patient Discharge , Pulmonary Fibrosis/virology , SARS-CoV-2/pathogenicity , Adolescent , Adult , COVID-19/complications , COVID-19/diagnosis , China , Female , Host-Pathogen Interactions , Humans , Lung/diagnostic imaging , Lung/physiopathology , Male , Middle Aged , Prognosis , Pulmonary Fibrosis/diagnostic imaging , Pulmonary Fibrosis/physiopathology , Respiratory Function Tests , Time Factors , Tomography, X-Ray Computed , Young Adult
Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors' signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.
Free chlorine is widely used as a disinfectant in the water industry. Accurate monitoring of the residual free chlorine concentration in water cycles is critical to maintain public health safety. Here, we report on a thin gold film-based reusable and reagent-less free chlorine sensor. A gold thin film of 300 nm thickness was deposited on a polyimide tape, which was placed on a glass substrate and a simple Styrofoam adhesive tape was used to cover the film and expose 0.36 cm2 circular area as the sensing surface. The sensor showed a high sensitivity of 0.327 µA ppm-1, with a linear range of 0 to 6 ppm, and an accuracy of <0.1 ppm with high selectivity in the presence of commonly interfering ions. The sensor response time was 50 s with a negligible hysteresis of 0.06 ppm. The sensor showed very little change in output current in the pH range between 5.2 to 8.4, and temperature range of 20 to 30 °C. Therefore, the sensor operation is reagent-less, does not need frequent calibration, and showed consistent sensing performance with real water samples. The simple fabrication, ease-of-use and reliable sensing performance of the proposed sensor shows feasibility for mass-production and application in remote and resource-limited areas.
Smart packaging of fresh produce is an emerging technology toward reduction of waste and preservation of consumer health and safety. Smart packaging systems also help to prolong the shelf life of perishable foods during transport and mass storage, which are difficult to regulate otherwise. The use of these ever-progressing technologies in the packaging of fruits has the potential to result in many positive consequences, including improved fruit quality, reduced waste, and associated improved public health. In this review, we examine the role of smart packaging in fruit packaging, current-state-of-the-art, challenges, and prospects. First, we discuss the motivation behind fruit quality monitoring and maintenance, followed by the background on the development process of fruits, factors used in determining fruit quality, and the classification of smart packaging technologies. Then, we discuss conventional freshness sensors for packaged fruits including direct and indirect freshness indicators. After that, we provide examples of possible smart packaging systems and sensors that can be used in monitoring fruits quality, followed by several strategies to mitigate premature fruit decay, and active packaging technologies. Finally, we discuss the prospects of smart packaging application for fruit quality monitoring along with the associated challenges and prospects.
We propose a wide-band metamaterial perfect absorber (MPA), using the coupling in the near-field of a quadruple split-ring resonator concentric with crossed ellipses. We designed the MPA with a metal-insulator-metal (MIM) structure for use in thermal energy harvesting. A gradient-based optimization approach was carried out to maximize the absorption of infrared (IR) radiation around 10 µm. Owing to the near-field coupling of resonators with optimal design parameters, the peaks of the absorption responses approach each other, thus broadening the overall bandwidth with almost unity absorptivity. The proposed design has a resonance at 10 µm resulting from magnetic polaritons (MPs) and thus maintains high absorption above 99% up to a range of incident-angles greater than 60° and exhibits a polarization-free behavior due to symmetry. When the optimal design was numerically examined to fabrication tolerances, it showed negligible sensitivities in the absorptivity with respect to design parameters. The strong electric field enhancement inside the split-ring gaps and between the ends of the cross arms and the surrounding ring enables designing MIM diodes to rectify the harvested thermal radiations at 288 K. MIM diodes can be built by the deposition of thin insulators to sit in these gaps. The MIM diode and MPA work together to harvest and rectify the incident IR radiation in a manner similar to the operation of rectennas. The MPA outperforms the traditional nano-antennas in impedance matching efficiency because of its higher resistance. Also, its dual-polarization reception capability doubles the rectenna efficiency. Our proposed MPA retained absorptivity more than 99% when coupled with MIM diodes whose resistances are in the range of 500 Ω-1 MΩ.
Screening and surveillance for gastrointestinal (GI) cancers by endoscope guided biopsy is invasive, time consuming, and has the potential for sampling error. Tissue endogenous fluorescence spectra contain biochemical and physiological information, which may enable real-time, objective diagnosis. We first briefly reviewed optical biopsy modalities for GI cancer diagnosis with a focus on fluorescence-based techniques. In an ex vivo pilot clinical study, we measured fluorescence spectra and lifetime on fresh biopsy specimens obtained during routine upper GI screening procedures. Our results demonstrated the feasibility of rapid acquisition of time-resolved fluorescence (TRF) spectra from fresh GI mucosal specimens. We also identified spectroscopic signatures that can differentiate between normal mucosal samples obtained from the esophagus, stomach, and duodenum.
