Wearable and implantable wireless sensor network solutions for healthcare monitoring

Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video

BACKGROUND

Although pose estimation algorithms have been used to analyze videos of patients with Parkinson's disease (PD) to assess symptoms, their feasibility for differentiating PD from other neurological disorders that cause gait disturbances has not been evaluated yet. We aimed to determine whether it was possible to differentiate between PD and spinocerebellar degeneration (SCD) by analyzing video recordings of patient gait using a pose estimation algorithm.

METHODS

We videotaped 82 patients with PD and 61 patients with SCD performing the timed up-and-go test. A pose estimation algorithm was used to extract the coordinates of 25 key points of the participants from these videos. A transformer-based deep neural network (DNN) model was trained to predict PD or SCD using the extracted coordinate data. We employed a leave-one-participant-out cross-validation method to evaluate the predictive performance of the trained model using accuracy, sensitivity, and specificity. As there were significant differences in age, weight, and body mass index between the PD and SCD groups, propensity score matching was used to perform the same experiment in a population that did not differ in these clinical characteristics.

RESULTS

The accuracy, sensitivity, and specificity of the trained model were 0.86, 0.94, and 0.75 for all participants and 0.83, 0.88, and 0.78 for the participants extracted by propensity score matching.

CONCLUSION

The differentiation of PD and SCD using key point coordinates extracted from gait videos and the DNN model was feasible and could be used as a collaborative tool in clinical practice and telemedicine.

Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.

An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

Wireless Battery-free and Fully Implantable Organ Interfaces

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

Comparative analysis of energy transfer mechanisms for neural implants

As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants.

Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

The Role of Wearable Sensors in the Future Primary Healthcare - Preferences of the Adult Swiss Population: A Mixed Methods Approach

Wearable sensors have the potential to increase continuity of care and reduce healthcare expenditure. The user concerns and preferences regarding wearable sensors are the least addressed topic in related literature. Therefore, this study aimed first, to examine the preferences of the adult Swiss population regarding the use of wearable sensors in primary healthcare. Second, the study aimed to explain and learn more about these preferences and why such wearable sensors would or would not be used. An explanatory sequential design was used to reach the two aims. In the initial quantitative phase preferences of a nationwide survey were analyzed descriptively and a multivariable ordered logistic regression was used to identify key characteristics, that influence the preferences. In the second phase, eight semi-structured interviews were conducted. The cleaned study sample of the survey included 687 participants, 46% of whom gave a positive rating regarding the use of wearable sensors. In contrast, 44% gave a negative rating and 10% were neutral. The interviews showed that sensors should be small, not flashy and be compatible with everyday activities. Individuals without a current health risk or existing chronic disease showed lower preferences for using wearable sensors, particularly because they fear losing control over their own body. In contrast, individuals with increased risk or with an existing chronic disease were more likely to use wearable sensors as they can increase the personal safety and provide real-time health information to physicians. Therefore, an important deciding factor for and against the use of wearable sensors seems to be the perceived personal susceptibility for potential health problems.

Advancements in Passive Wireless Sensors, Materials, Devices, and Applications

In recent years, passive wireless sensors have been studied for various infrastructure sectors, making them a research and development focus. While substantial evidence already supports their viability, further effort is needed to understand their dependability and applicability. As a result, issues related to the theory and implementation of wireless sensors still need to be resolved. This paper aims to review and summarize the progress of the different materials used in different passive sensors, the current status of the passive wireless sensor readout devices, and the latest peripheral devices. It will also cover other related aspects such as the system equipment of passive wireless sensors and the nanogenerators for the energy harvesting for self-powered sensors for applications in contemporary life scenarios. At the same time, the challenges for future developments and applications of passive wireless are discussed.

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review

Rockfalls and landslide events are caused by different factors among which are included geomorphological and climatic factors and also human interaction. Therefore, the economic and social impacts can be significant and the remote monitoring of such hazards has become an essential topic in various applications. Wireless sensor networks (WSNs) are well suited for the deployment of monitoring systems, benefiting from the different technologies and topologies that are available and evolving nowadays. This review paper aims to summarize and overview the up-to-date state of the art of rockfall and landslide monitoring systems based on WSNs. The implementation and methods were analyzed for each solution, along with the system architecture and relevant hardware aspects. All the retrieved data were used to analyze the current trends and future possibilities in the field of WSN geohazard monitoring.

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.

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients.

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.

Next generation smartwatches to estimate whole body composition using bioimpedance analysis: accuracy and precision in a diverse multiethnic sample

BACKGROUND

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of devices are unknown.

OBJECTIVES

This study evaluated smart watches with integrated bioimpedance (BIA) sensors for their ability to measure and monitor change in body composition.

DESIGN

Participants recruited across body mass indexes received duplicate body composition measures using two wearable smart watch (W-BIA) models in sitting and standing positions and multiple versions of each watch were used to evaluate inter- and intra-model precision. Duplicate laboratory-grade octapolar bioimpedance (8-BIA) and criterion dual-energy X-ray absorptiometry (DXA) scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor change in body composition.

RESULTS

Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (p < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA, p > 0.05, CCC = 0.97). FFM was less precise on the watches than DXA (CV = 0.7%, RMSE = 0.4 kg versus CV = 1.3%, RMSE = 0.7 kg for W-BIA), requiring more repeat measures to equal the same confidence in body composition change over time as DXA.

CONCLUSIONS

After systematic correction, smart watch BIA devices are capable of stable, reliable and accurate body composition with precision comparable but lower than laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems such as the home, training centers, and geographically remote locations.

An Efficient Emergency Patient Monitoring Based on Mobile Ad Hoc Networks

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.

A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data

Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96-99% accuracy in evaluating the prognosis of Parkinsonian gaits.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare-A Review

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.

Real-time tracking of a moving target in an indoor corridor of the hospital building using RSSI signals received from two reference nodes

In this paper, implementation and validation of a target tracking system based on the received signal strength indicator (RSSI) for an indoor corridor environment of the hospital is presented. Six tracking methods of a moving target (i.e., equipment, robot, or human) using RSSI signals measured from two stationary reference nodes located at the different sides of the corridor are proposed. A filter with its optimal weight value is also applied to smoothen and increase the accuracy of estimated position results (i.e., the x-position in the corridor). Additionally, a determination approach for finding the optimal parameters assigned for the proposed tracking methods and the filter are also introduced. The proposed methods are implemented in MATLAB/Simulink, and experiments using a 2.4 GHz, IEEE 802.15.4/ZigBee wireless network have been carried out in the indoor corridor of the hospital building. Experimental results obtained from the corridor size of 22 m demonstrate that our proposed methods can automatically and efficiently track the moving target in real time. The average distance errors, in the case of varying and manual tuning the optimal parameters of the proposed methods and the filter, reduce from 5.14 to 1.01 m and 4.55 to 0.86 m (i.e., two test cases; slow moving speed and double moving speed). Here, the errors decrease by 80.35% and 81.10%, respectively. For the case using the optimal parameters determined by the optimization approach, the average errors can reduce to 0.97 m for the first test case and 0.78 m for the second test case, respectively.

Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review

Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual's mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson's disease, Alzheimer's disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson's cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials.

Smart Textiles for Improved Quality of Life and Cognitive Assessment

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs.

Risk factor management of atrial fibrillation using mHealth: The Atrial Fibrillation – Helping Address Care with Remote Technology (AF-HEART) Pilot Study

Background

Personalized treatment of atrial fibrillation (AF) risk factors using mHealth and telehealth may improve patient outcomes.

Objective

The purpose of this study was to assess the feasibility of the Atrial Fibrillation Helping Address Care with Remote Technology (AF-HEART) intervention on the following patient outcomes: (1) heart rhythm tracking; (2) weight, alcohol, blood pressure (BP), and sleep apnea reduction; (3) AF symptom reduction; and (4) quality-of-life (QOL) improvement.

Methods

A total of 20 patients with AF undergoing antiarrhythmic therapy, cardioversion, and/or catheter ablation were enrolled and followed for 6 months. The AF-HEART intervention included remote heart rhythm, weight, and BP tracking; televisits with a dietician focusing on AF risk factors; and referrals for sleep apnea and hypertension treatment.

Results

Patients transmitted a median of 181 rhythm recordings during the 6-month follow-up period. Patients lost an average of 3.5 kilograms at 6 months (P = .005). Patients had improved SF-12 scores (P = .01), AFSS score (P = .01), EQ-5D score (P = .006), and AFEQT Global Score (P = .03). There was significant correlation between weight loss and decrease in symptom severity (r = -0.45, P = .05), and between % weight loss and decrease in symptom severity (r = -0.49, P = .03).

Conclusion

This study described the feasibility of the AF-HEART intervention for (1) consistent remote tracking of heart rhythm, weight, and BP; (2) achievement of weight loss; (3) reduction of symptoms; and (4) improvement in QOL. Expansion to a larger randomized study is planned.

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

Challenges and Developments in Secure Routing Protocols for Healthcare in WBAN: A Comparative Analysis

The rise in life expectancy of humans, COVID-19 pandemic and growing cost of medical services has brought up huge challenges for the government and healthcare industry. Due to unhealthy lifestyle, there is an increased need for continual health monitoring and diagnosis of diseases. Wireless Body Area Network (WBAN) is attracted attention of researchers as various biosensors can be embedded in or worn on the body of human beings for the measurement of health parameters. The patient's health data is then sent wirelessly to the physician for health analysis. The biosensors used to measure physiological parameters have limited power due to its small size and hence smaller form factor. For the longevity of the network, it is imperative to transmit the data in an energy-efficient manner. Moreover, the health information of the patient is stringently private. Hence, the privacy and security of transmitted information needs to be ensured. It necessitates the development of effective, lightweight and secure routing protocols that provides security with minimal use of resources. This paper has identified the numerous security requirements in WBANs and has provided the extensive review on existing secure routing protocols reported in the literature. A comparative analysis of the various existing state-of-the art secure routing protocols and critical analysis based on security techniques along with different performance parameters has been presented.

Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study

Owing to automation trends, research on wireless sensor networks (WSNs) has become prevalent. In addition to static sinks, ground and aerial mobile sinks have become popular for data gathering because of the implementation of WSNs in hard-to-reach or infrastructure-less areas. Consequently, several data-gathering mechanisms in WSNs have been investigated, and the sink type plays a major role in energy consumption and other quality of service parameters, such as packet delivery ratio, delay, and throughput. However, the data-gathering schemes based on different sink types in WSNs have not been investigated previously. This paper reviews such data-gathering frameworks based on three different types of sinks (i.e., static, ground mobile, and aerial mobile sinks), analyzing the data-gathering frameworks both qualitatively and quantitatively. First, we examine the frameworks by discussing their working principles, advantages, and limitations, followed by a qualitative comparative study based on their main ideas, optimization criteria, and performance evaluation parameters. Next, we present a simulation-based quantitative comparison of three representative data-gathering schemes, one from each category. Simulation results are shown in terms of energy efficiency, number of dead nodes, number of exchanged control packets, and packet drop ratio. Finally, lessons learned from the investigation and recommendations made are summarized.

Next-Generation Continuous Metabolite Sensing toward Emerging Sensor Needs

This article discusses the emergent biosensor technology focused on continuous biosensing of metabolites by non-invasive sampling of body fluids emphasized on physiological monitoring in mobility-constrained populations, resource-challenged settings, and harsh environments. The boom of innovative ideas and endless opportunities in healthcare technologies has transformed traditional medicine into a sustainable link between medical practitioners and patients to provide solutions for faster disease diagnosis. The future of healthcare is focused on empowering users to manage their own health. The confluence of big data and predictive analysis and the internet of things (IoT) technology have shown the potential of converting the abundant health profile data amassed from medical diagnosis of patients into useable information, whilst allowing caregivers to provide suitable treatment plans. The implementation of the IoT technology has opened up advanced approaches in real-time, continuous, remote monitoring of patients. Wearable, point-of-care biosensors are the future roadmap to providing direct, real-time information of health status to the user and medical professionals in this digitized era.

Electrode and electrolyte configurations for low frequency motion energy harvesting based on reverse electrowetting

Increasing demand for self-powered wearable sensors has spurred an urgent need to develop energy harvesting systems that can reliably and sufficiently power these devices. Within the last decade, reverse electrowetting-on-dielectric (REWOD)-based mechanical motion energy harvesting has been developed, where an electrolyte is modulated (repeatedly squeezed) between two dissimilar electrodes under an externally applied mechanical force to generate an AC current. In this work, we explored various combinations of electrolyte concentrations, dielectrics, and dielectric thicknesses to generate maximum output power employing REWOD energy harvester. With the objective of implementing a fully self-powered wearable sensor, a “zero applied-bias-voltage” approach was adopted. Three different concentrations of sodium chloride aqueous solutions (NaCl-0.1 M, NaCl-0.5 M, and NaCl-1.0 M) were used as electrolytes. Likewise, electrodes were fabricated with three different dielectric thicknesses (100 nm, 150 nm, and 200 nm) of Al₂O₃ and SiO₂ with an additional layer of CYTOP for surface hydrophobicity. The REWOD energy harvester and its electrode–electrolyte layers were modeled using lumped components that include a resistor, a capacitor, and a current source representing the harvester. Without using any external bias voltage, AC current generation with a power density of 53.3 nW/cm² was demonstrated at an external excitation frequency of 3 Hz with an optimal external load. The experimental results were analytically verified using the derived theoretical model. Superior performance of the harvester in terms of the figure-of-merit comparing previously reported works is demonstrated. The novelty of this work lies in the combination of an analytical modeling method and experimental validation that together can be used to increase the REWOD harvested power extensively without requiring any external bias voltage.

Wearable and Implantable Intraocular Pressure Biosensors: Recent Progress and Future Prospects

Biosensors worn on or implanted in eyes have been garnering substantial attention since being proven to be an effective means to acquire critical biomarkers for monitoring the states of ophthalmic disease, diabetes. Among these disorders, glaucoma, the second leading cause of blindness globally, usually results in irreversible blindness. Continuous intraocular pressure (IOP) monitoring is considered as an effective measure, which provides a comprehensive view of IOP changes that is beyond reach for the "snapshots" measurements by clinical tonometry. However, to satisfy the applications in ophthalmology, the development of IOP sensors are required to be prepared with biocompatible, miniature, transparent, wireless and battery-free features, which are still challenging with many current fabrication processes. In this work, the recent advances in this field are reviewed by categorizing these devices into wearable and implantable IOP sensors. The materials and structures exploited for engineering these IOP devices are presented. Additionally, their working principle, performance, and the potential risk that materials and device architectures may pose to ocular tissue are discussed. This review should be valuable for preferable structure design, device fabrication, performance optimization, and reducing potential risk of these devices. It is significant for the development of future practical IOP sensors.

Super-Nernstian pH Sensor Based on Anomalous Charge Transfer Doping of Defect-Engineered Graphene

The conventional pH sensor based on the graphene ion-sensitive field-effect transistor (Gr-ISFET), which operates with an electrostatic gating at the solution-graphene interface, cannot have a pH sensitivity above the Nernst limit (∼59 mV/pH). However, for accurate detection of the pH levels of an aqueous solution, an ultrasensitive pH sensor that can exceed the theoretical limit is required. In this study, a novel Gr-ISFET-based pH sensor is fabricated using proton-permeable defect-engineered graphene. The nanocrystalline graphene (nc-Gr) with numerous grain boundaries allows protons to penetrate the graphene layer and interact with the underlying pH-dependent charge-transfer dopant layer. We analyze the pH sensitivity of nc-Gr ISFETs by adjusting the grain boundary density of graphene and the functional group (OH-, NH₂-, CH₃-) on the SiO₂ surface, confirming an unusual negative shift of the charge-neutral point (CNP) as the pH of the solution increases and a super-Nernstian pH response (approximately -140 mV/pH) under optimized conditions.

Wireless body area network mutual trust analysis technique for fault detection using software defined network (SDN)

Wireless Sensor Node (WSN) helps to track inpatient and remote patient (home/working) health information. Mishandling of the electronic system, patient behaviour and environmental changes which are all lead to incorrect data generation while using WSN for medical purposes. It leads to a false alarm being raised, network resource wastage, a false node priority level and low reliability. We have introduced the Mutual Trust Model (MTM) for Wireless Body Area Network (WBAN) with the help of Fog-Node (FN) to address these issues and to ensure the trustworthiness of the information acquired. In this, First-Hand Trust Method calculates the confidence value of the individual sensor node. Then, with neighbor node support, the Stigmercy Trust Method (STM) is implemented to reinforce the trust source node. Ultimately, the individual patient’s confidence value for the MTM model is determined. With the assistance of the wireless-mininet network emulator and the RYU controller, the network environment model implement, and the results have been obtained. MTM predicts the confidence level of the collected data significantly and produces an accuracy of 92.3 percentage to prevent the emergency band from being used dispensable.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

A Hydrogel-Based Ultrasonic Backscattering Wireless Biochemical Sensing

Wireless monitoring of the physio-biochemical information is becoming increasingly important for healthcare. In this work, we present a proof-of-concept hydrogel-based wireless biochemical sensing scheme utilizing ultrasound. The sensing system utilizes silica-nanoparticle embedded hydrogel deposited on a thin glass substrate, which presents two prominent interfaces for ultrasonic backscattering (tissue/glass and hydrogel/glass). To overcome the effect of the varying acoustic properties of the intervening biological tissues between the sensor and the external transducer, we implemented a differential mode of ultrasonic back-scattering. Here, we demonstrate a wireless pH measurement with a resolution of 0.2 pH level change and a wireless sensing range around 10 cm in a water tank.

A smart wearable device for monitoring and self-management of diabetic foot: A proof of concept study

BACKGROUND AND OBJECTIVE

Diabetic foot is one of the important complications of diabetes, which is occurred due to the destructive parameters in different anatomical sites of feet. Management and monitoring of these parameters are very important to decrease or prevent foot ulcers. We aimed to develop a smart wearable device to monitor these parameters to prevent diabetic foot.

METHODS

Following literature review and expert panel discussions, we considered pressure, temperature and humidity to develop the system. During these sessions, we also developed the system architecture and determined the required technologies. We also developed a mobile application. Finally, all sensors were evaluated for accurate monitoring of pressure, temperature and humidity. A standard protocol was used to evaluate each of these sensors. To this end, five people (four with diabetes and one healthy person) participated. They did a series of movements including walking, sitting, and standing. We considered the pressure measured by Pedar system as the gold standard. Furthermore, we changed the environment temperature and humidity during several experiments and considered the environment temperature and humidity as gold standard. We compared the measured values by sensors with these gold standards.

RESULTS

The evaluation indicated the accurate performance of pressure, humidity and temperature sensors. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the system to provide alarms based on the pressure measured using Pedar were 100, 50, 92.5, 91.8, and 100 %, respectively. The performance of temperature sensors in smart shoes was confirmed by slight differences compared to thermometers. Relatively equal values of humidity measured by two sensors on the left and right feet and the increased difference with the environment humidity showed the exact humidity measured using these sensors.

CONCLUSION

This smart shoes monitors pressure, humidity, and temperature of patients' feet and sends this data to their smart phone by the Bluetooth module. Furthermore, it controls these parameters; as each of these parameters exceeds the defined threshold, alerts are given to patients for self-management.

Ultrasonic Hydrogel Biochemical Sensor System

In this work, we present a proof-of-concept hydrogel-based sensor system capable of wireless biochemical sensing through measuring backscattered ultrasound. The system consists of silica-nanoparticle embedded hydrogel deposited on a thin glass substrate, presenting two interfaces for backscattering (tissue/hydrogel and hydrogel/glass), which allows for system output to be invariant under the change in acoustic properties (e.g. attenuation, reflection) of the intervening biological tissue. We characterize the effect of silica nanoparticles (acoustic contrast agents) loading on the hydrogel's swelling ratio and its ultrasonic backscattering properties. We demonstrate a wireless pH measurement using dual modes of interrogations, reflection ratio and time delay. The ultrasonic hydrogel pH sensor is demonstrated with a sensing resolution of 0.2 pH level change with a wireless sensing distance around 10 cm.

The development and testing of a nurse-led smartphone-based self-management programme for diabetes patients with poor glycaemic control

AIMS

To describe a systematic process for the development of a nurse-led smartphone-based self-management programme for type 2 diabetes patients with poor glycaemic control in Singapore.

METHODS

A three-step process involving the application of a theoretical framework, evidence from literature, content validity, and pilot tests were conducted for the content and technical development of the programme. Content experts and lay patients evaluated the appropriateness, relevance, and comprehensibility of the newly developed Care4Diabetes application. A pilot randomized controlled trial was conducted with 40 patients recruited in Singapore. Twenty patients each were randomly allocated to the control and intervention groups. The study outcomes were collected at baseline and at 3 months thereafter.

RESULTS

The nurse-led smartphone-based self-management programme was developed with integration of the Care4Diabetes application and the web-portal system. The pilot results indicated that the effects of this smartphone-based programme on patient's health-related outcomes were comparable with those of the currently available nurse-led diabetes service.

CONCLUSION

The smartphone-based self-management intervention was deemed effective, yet full-scale randomized controlled trials are still ongoing and the results of these may provide strong evidence of the effectiveness of such an approach in improving patient care.

IMPACT

The uniqueness of this study lies in the integrated system used, which offers a clinical platform for diabetes nurses to provide personalized coaching and care to patients remotely, while monitoring patients' progress closely. By adopting such an approach, it would free up more time for nurses to cater to patients who are more critically in need of their direct attention.

Control Plane Optimisation for an SDN-Based WBAN Framework to Support Healthcare Applications

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.

Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review

Green processes are very important for the implementation of green technologies in production to achieve positive sustainability outcomes in the Industry 4.0 era. The scope of the paper is to review how conventional green processes as a part of Industry 4.0 provide sustainability outcomes in manufacturing. The paper is based on the methodology of systematic literature review through the content analysis of literary resources. Twenty-nine studies were included in our content analysis. The results show the main focus of current literature related to Industry 4.0, sustainability outcomes and green processes. The authors present a conceptual Sustainability Green Industry 4.0 (SGI 4.0) framework that helps to structure and evaluate conventional green processes in relation to Industry 4.0 and sustainability. The study summarizes which technologies (big data, cyber-physical systems, Industrial Internet of Things and smart systems) and green processes (logistics, manufacturing and product design) are important for achieving a higher level of sustainability. The authors found that the most often common sustainability outcomes are energy saving, emission reduction, resource optimalization, cost reduction, productivity and efficiency and higher economic performance, human resources development, social welfare and workplace safety. The study suggests implications for practice, knowledge and future research.

Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review

BACKGROUND

Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date.

OBJECTIVE

The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs.

METHODS

A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool.

RESULTS

In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate.

CONCLUSIONS

Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring.