Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

The Influence of the Washing Process on the Impedance of Textronic Radio Frequency Identification Transponder Antennas

Antennas dedicated to RFID systems created on textile substrates should maintain strictly defined parameters. During washing, the materials from which such antennas are made are exposed to mechanical and chemical exposure-degradation of the parameters characterizing those materials may occur, which in turn may lead to a change in the parameters of the antenna. For research purposes, four groups of model dipole antennas (sewn with two types of conductive threads on two fabrics) were created and then they were subjected to several washing processes. After each stage of the experiment, the impedance parameters of the demonstration antennas were measured using indirect measurements. Based on the obtained results, it was found that these parameters change their values during washing, and that this is influenced by a number of factors, e.g., shrinkage of the substrate fabric.

Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation

Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot's position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review

BACKGROUND

As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety.

OBJECTIVE

This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors?

METHODS

We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility.

RESULTS

Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting.

CONCLUSIONS

A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

The deployment of wearable or body-worn devices is increasing rapidly, and thus researchers' interests mainly include technical and economical issues, such as networking, interoperability, security, power optimization, business growth and regulation. To address these issues properly, previous survey papers usually focused on describing the wireless body area network architecture and network protocols. This implies that deployment issues and awareness issues of wearable and BAN devices are not emphasized in previous work. To defeat this problem, in this study, we have focused on feasibility, limitations, and security concerns in wireless body area networks. In the aspect of the economy, we have focused on the compound annual growth rate of these devices in the global market, different regulations of wearable/wireless body area network devices in different regions and countries of the world and feasible research projects for wireless body area networks. In addition, this study focuses on the domain of devices that are equally important to physicians, sportsmen, trainers and coaches, computer scientists, engineers, and investors. The outcomes of this study relating to physicians, fitness trainers and coaches indicate that the use of these devices means they would be able to treat their clients in a more effective way. The study also converges the focus of businessmen on the Annual Growth Rate (CAGR) and provides manufacturers and vendors with information about different regulatory bodies that are monitoring and regulating WBAN devices. Therefore, by providing deployment issues in the aspects of technology and economy at the same time, we believe that this survey can serve as a preliminary material that will lead to more advancements and improvements in deployment in the area of wearable wireless body area networks. Finally, we present open issues and further research direction in the area of wireless body area networks.

A method of classification decision based on multi-BiLSTMs for physical loads hierarchy

Human gait is systematically deformed by physical loads, this study constructs and evaluates an algorithm for classifying different levels of physical loads. The algorithm uses wearable IMUs data to classify different levels of loads. We aim to evaluate classification as strategy for multi-loads recognition for the control of wearable exoskeletons. 10 adults participated in the experiment. In the experiment, the subjects walked on flat ground carrying a backpack with different weight of loads (0, 15 and 25 kg), and three sensors on the lower limbs collected the subjects' gait data in real time. In this study, a method of classification decision based on multiple bidirectional long short-term memory(multi-BiLSTMs) was proposed which was used to classify the load level of the collected data. The classification accuracy of this method reached 94.1%, and the F-score was 0.935-0.952. Compared with LSTM and BiLSTM, the proposed method has better performance in accuracy of load classification. The results of this study contribute to quantify the load, which has promising applications in the medical and labor protection fields.

A Photoplethysmogram Dataset for Emotional Analysis

In recent years, research on emotion classification based on physiological signals has actively attracted scholars’ attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studying PPGs for emotional analysis is the lack of open datasets (there is a single dataset to the best of the authors). This study introduces a new PPG dataset for emotional analysis. A total of 72 PPGs were recorded from 18 participants while watching short video clips and analyzed in time and frequency domains. Moreover, emotional classification accuracies with the presented dataset were presented with various neural network structures. The results prove that this dataset can be used for further emotional analysis with PPGs.

Applications and Innovations on Sensor-Enabled Wearable Devices

Multiple sensors are embedded in wearable devices [...]

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Wearable and Implantable Soft Bioelectronics: Device Designs and Material Strategies

High-performance wearable and implantable devices capable of recording physiological signals and delivering appropriate therapeutics in real time are playing a pivotal role in revolutionizing personalized healthcare. However, the mechanical and biochemical mismatches between rigid, inorganic devices and soft, organic human tissues cause significant trouble, including skin irritation, tissue damage, compromised signal-to-noise ratios, and limited service time. As a result, profuse research efforts have been devoted to overcoming these issues by using flexible and stretchable device designs and soft materials. Here, we summarize recent representative research and technological advances for soft bioelectronics, including conformable and stretchable device designs, various types of soft electronic materials, and surface coating and treatment methods. We also highlight applications of these strategies to emerging soft wearable and implantable devices. We conclude with some current limitations and offer future prospects of this booming field.

Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

Multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster-Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster's combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc.

Smart Fibrous Structures Produced by Electrospinning Using the Combined Effect of PCL/Graphene Nanoplatelets

Over the years, the development of adaptable monitoring systems to be integrated into soldiers’ body gear, making them as comfortable and lightweight as possible (avoiding the use of rigid electronics), has become essential. Electrospun microfibers are a great material for this application due to their excellent properties, especially their flexibility and lightness. Their functionalization with graphene nanoplatelets (GNPs) makes them a fantastic alternative for the development of innovative conductive materials. In this work, electrospun membranes based on polycaprolactone (PCL) were impregnated with different GNPs concentrations in order to create an electrically conductive surface with piezoresistive behavior. All the samples were properly characterized, demonstrating the homogeneous distribution and the GNPs’ adsorption onto the membrane’s surfaces. Additionally, the electrical performance of the developed systems was studied, including the electrical conductivity, piezoresistive behavior, and Gauge Factor (GF). A maximum electrical conductivity value of 0.079 S/m was obtained for the 2%GNPs-PCL sample. The developed piezoresistive sensor showed high sensitivity to external pressures and excellent durability to repetitive pressing. The best value of GF (3.20) was obtained for the membranes with 0.5% of GNPs. Hence, this work presents the development of a flexible piezoresistive sensor, based on electrospun PCL microfibers and GNPs, utilizing simple methods.

Recent Advances in Noninvasive Biosensors for Forensics, Biometrics, and Cybersecurity

Recently, biosensors have been used in an increasing number of different fields and disciplines due to their wide applicability, reproducibility, and selectivity. Three large disciplines in which this has become relevant has been the forensic, biometric, and cybersecurity fields. The call for novel noninvasive biosensors for these three applications has been a focus of research in these fields. Recent advances in these three areas has relied on the use of biosensors based on primarily colorimetric assays based on bioaffinity interactions utilizing enzymatic assays. In forensics, the use of different bodily fluids for metabolite analysis provides an alternative to the use of DNA to avoid the backlog that is currently the main issue with DNA analysis by providing worthwhile information about the originator. In biometrics, the use of sweat-based systems for user authentication has been developed as a proof-of-concept design utilizing the levels of different metabolites found in sweat. Lastly, biosensor assays have been developed as a proof-of-concept for combination with cybersecurity, primarily cryptography, for the encryption and protection of data and messages.

An On-Chip Processor for Chronic Neurological Disorders Assistance Using Negative Affectivity Classification

Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but their severe effects can be alleviated by early preemptive measures. CND's, such as Alzheimer's, Autism Spectrum Disorder (ASD), and Amyotrophic Lateral Sclerosis (ALS), are the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long term continuous monitoring with neuro-feedback of human emotions for patients with CND's is crucial in mitigating its harmful effect. This paper presents hardware efficient and dedicated human emotion classification processor for CND's. Scalp EEG is used for the emotion's classification using the valence and arousal scales. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio, and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable non-invasive classification system. A look-up-table based logarithmic division unit (LDU) is to represent the division features in machine learning (ML) applications. The implemented LDU minimizes the cost of integer division by 34% for ML applications. The implemented emotion's classification processor achieved an accuracy of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available datasets. The 2 x 3mm² processor is fabricated using a 0.18 μm 1P6M CMOS process with power and energy utilization of 2.04 mW and 16 μJ/classification, respectively, for 8-channel operation.

Telehealth, Telemedicine, and Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) telehealth management may improve initial and chronic care access, time to diagnosis and treatment, between-visit care, e-communications and e-education, workflows, costs, and therapy outcomes. OSA telehealth options may be used to replace or supplement none, some, or all steps in the evaluation, testing, treatments, and management of OSA. All telehealth steps must adhere to OSA guidelines. OSA telehealth may be adapted for continuous positive airway pressure (CPAP) and non-CPAP treatments. E-data collection enhances uses for individual and group analytics, phenotyping, testing and treatment selections, high-risk identification and targeted support, and comparative and multispecialty therapy studies.