Recent Trends in Remote Healthcare Applications and Futuristic Approach

Remote healthcare is a well-accepted telemedicine service that renders efficient and reliable healthcare to patients suffering from chronic diseases, neurological disorders, diabetes, osteoporosis, sensory organs, and other ailments. Artificial intelligence, wireless communication, sensors, organic polymers, and wearables enable affordable, non-invasive healthcare to patients in all age groups. Telehealth services and telemedicine are beneficial to people residing in remote locations or patients with limited mobility, rehabilitation treatment, and post-operative recovery. Remote healthcare applications and services proved to be significant during the COVID-19 pandemic for both patients and doctors. This study presents a detailed study of the use of artificial intelligence and the internet of things in applications of remote healthcare in many domains of health, along with recent patents. This research also presents network diagrams of documents from the Scopus database using the tool VOSViewer. The paper highlights gap which can be undertaken by future researchers. © 2023 IEEE.

Acceptable nudge strategies to incentivize the use of wearables and physiolytics at work: A Q-methodology examination

The Covid-19 pandemic has increased the pressure on organizations to ensure health and safety in the workplace. An increasing number of organizations are considering wearables and physiolytics devices as part of their safe return to work programs so as to comply with governments' accountability rules. As with other technologies with ambivalent use (i.e., simultaneously beneficial and harmful), the introduction of these devices in work settings is met with skepticism. In this context, nudging strategies as a way of using design, information, and other ways to manipulate behaviors (system 1 nudge) and choices (system 2 nudge) has gained traction and is often applied alongside the introduction of ambivalent technologies with the aim to "nudge” their use. While the feasibility of different nudge strategies is often studied from only a managerial perspective, where employees' volitional autonomy and dignity is often treated as secondary, we explore which nudges are acceptable from the perspectives of ordinary workers. Using Q-methodology as a more evolutionary and participatory way to design nudges, we describe five basic strategies that are (to varying degrees) acceptable to them: (a) positive reinforcement and fun, (b) controlling the organizational environment, (c) self-responsibility, (d) collective responsibility, and (e) adapting the individual environment. Our findings show that there is a wide range of viewpoints on what is being considered an acceptable nudge and stress the importance of a transparent, equal dialogue between those who design nudges and potential nudgees. © Association for Information Technology Trust 2023.

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.

Watching me watching you: Training and assessment in a virtual world.

Virtual education isn't new. Teaching has been delivered remotely for many years, although it came to prominence with the enforced move to online learning during the COVID-19 pandemic. Technologies provide tools that are integral to our everyday lives, and training and assessment are no different. Stepping back from the obligatory remote delivery of practical skills training, now we have the opportunity to review and evaluate our own learning (as educators) about the value of resources for training delivery. Combining sound educational principles with the available technologies, we can use remotely delivered learning to enhance our teaching and to increase access to learning, without hindrance because of distance or the type of facility. Remote connections allow expertise and best practice to become suddenly available to a wider cohort of researchers, rapidly spreading new ideas about refinement of in vivo procedures and supporting colleagues to learn and develop. We currently use only a few of the technological tools available, and there is much to learn from other disciplines where virtual and augmented reality are assisting surgeons, aircraft pilots and others daily. By harnessing remote and assisted technologies in teaching, we can also develop the mindset and ability of the biomedical community to use them to augment, or even to replace, animal studies in future and to democratise training globally.

Biodegradable Bio-based Material for Wearable Air Filtering Skin

COVID-19, imagine having a temporary lip sticker that offers the protection of an n95 mask without the uncomfortable bulk. Using green electrospun nanofibers the lip sticker filters the virus and can communicate geospatial data to your phone using embedded NFC technology. Available in different designs and skins, some fiber formations can display temperature changes on your face. This paper investigates several prototypes of the described product. © 2022 Owner/Author.

Preliminary Investigation on Wrist Temperature Control by Wearable Peltier Armband

Due to COVID-19 pandemic, body temperature measurement in commercial facilities is performed using a non-contact method. However, if the human body can be controlled in some way to disguise body temperature, a thermometer would have difficulty detecting an entrant with a fever. In this study, we propose a method to control body temperature measured at the wrist in order to demonstrate the vulnerability of temperature measurement at the wrist. Our device lowers body temperature by cooling the upper arm, thereby cooling blood flow and reducing the intensity of infrared radiation detected by a thermometer. The implemented device was used to cool the upper arm under three different conditions. The results showed that cooling the blood flow in the upper arm can lower the body temperature at the wrist. The cooled body temperature was difficult to maintain after the end of cooling, irrespective of the cooling intensity and cooling time. © 2022 ACM.

Advances in medical textiles

Medical textile is one of the technical textiles sectors, growing faster due to developments in polymer science and technology and innovation in forming new textile structures. In this review, current market trends for the growth of medical textiles for both pre and post covid pandemic periods were discussed. Focus is given to the classification of medical textiles and devices, specific requirements of fibers and widely used types of fibers, and advanced developments in this field, including nanofibers, bicomponent fibers, superabsorbent polymers, and conductive materials used in a wide range of advanced medical devices. Various fabric structures (woven/knitted/nonwoven/braided) have been in use in biomedical devices;however, recent 3D shaped structures such as spacer fabrics, and 3D-printed materials have profoundly marked their significance with its ability to adapt to specific needs of the medical community. Smart wearable sensor technologies for monitoring, diagnosis, and treatment are discussed and critically reviewed, enabling the readers to understand the complexity of the nature of interdisciplinary approaches required for developing such complex structures and systems. Antimicrobial agents (synthetic and natural/organic) used in the development of medical textiles mainly wound dressings, advances in antiadhesive textile coatings, and antimicrobial assessments of medical fabrics are critically reviewed. Finally, a case study on 3D printing of complex structures is presented to update modern developments using fine detail resolution (FDR), a selective laser sintering that uses carbon dioxide laser to produce delicate and complex 3D structures suitable for medical applications. It is anticipated that readers will benefit from this critical overview of trends in this sector and the multidisciplinary approaches needed to meet the demands of the ever-growing consumer base. © 2023 Elsevier Ltd. All rights reserved.

Anatomy of an American football game: Player-to-player contact before, during and after an NFL game in context of the 2020 COVID-19 pandemic.

OBJECTIVES: To quantify levels of potential exposure to SARS-CoV-2 surrounding a typical professional American football game, with a focus on interactions on-field between teammates and opposing players before, during, and immediately after competition. METHODS: We examined across-Club consecutive interactions ≥2 minutes within 6 feet [1.8 meters] between athletes on opposing Clubs for all 2020 NFL regular season games (n = 256). Cumulative interaction was measured for a representative subset (n = 119; 46%) of games. Wearable proximity tracking devices (Kinexon) were used to measure distance and duration of interactions; these data were combined with game schedule and Club rosters for analyses. Frequency and per-game mean, median, interquartile range for consecutive interactions ≥2/≥5 minutes and cumulative interactions ≥5/≥15 were described overall and stratified by pre-game, in-game, and post-game. RESULTS: Of the 1964 distinct player-to-opponent contacts ≥2 minutes in NFL regular season games, the majority (n = 1,699; 87%) were fewer than 5 minutes in consecutive length. Among the mean 7.7 distinct contacts ≥2 minutes with opponents each game (median = 4; IQR = 2, 8), very few were ≥5 consecutive minutes at any point (mean = 1.0; median = 0; IQR = 0, 0). Most (n = 849; 43.2%) distinct contacts were pre-game, 546 (27.8%) were during competition, and 569 (29%) were post-game. In games where cumulative interactions were analyzed, there was an average of 17.1 player/opponent interactions with cumulative exposure ≥5 minutes (median = 12; IQR = 4, 30), almost all of which occurred during competition. CONCLUSION: There is limited and short contact between and among competing players in professional American football. In the setting of infectious disease such as the COVID-19 pandemic, a robust prevention program integrating masking, distancing, hygiene, and ventilation when off-field can be created to minimize on- and off-field exposures, which effectively reduces transmission risk in outdoors and/or well-ventilated stadium settings.

Healthcare Industry: Embracing Potential of Big Data across Value Chain

In the fast-moving era of the Industrial Revolution (Industry 4.0), digitally fueled devices and technologies are paramount for driving innovation and creating values across a myriad of industries. A case in point is - Healthcare Industry. Healthcare insurance companies, hospitals, and other providers around the world are belligerently leveraging digital tools and technologies such as Big Data analytics, Lake, Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing, smart sensors, and the Internet of Things (IoT), for improving the overall quality of care and overall process efficiency and effectiveness. The Healthcare industry has been a center of discussion for embracing Big Data practice across the value chain for the past couple of decades due to the prodigious potential that is concealed in it. With so much abundant information, there have been numerous provocations related to the apiece stage of maneuvering big data that can only be amplified by leveraging high-end computer science results for big data analytics, as mentioned above. Well-organized healthcare ecosystem, analysis, and magnification of big data can influence the course of the game by opening new paths in terms of offering unique yet innovative products and services for the modern age technology-propelled healthcare value chain. This paper emphasizes the impetus of Big Data across the healthcare value chain, which involves the amalgamation of technology, data, and business, yielding better decisions and improving the experience across all touch points. © 2022 IEEE.

Development of Elbow-Wrist Telerehabilitation System - An Affordable, Available, Accessible, and Acceptable (4As) System

The application of telerehabilitation system has gained popularity and acceptance recently due to the restrictions in controlling the COVID-19 pandemic. This paper described the development of an elbow-wrist telerehabilitation system that complement the disrupted routine rehabilitation session. The developed system consists of a wearable exoskeleton system that assist in rehabilitation of the elbow and wrist joints for individuals with neurological conditions such as Parkinson's and Spinal Cord Injuries that affects movements of the upper extremities. The two modes of operation available enables the adoption of the 5G technology in the near future. This system also potentially fulfills the requirement of Accessibility, Availability, Affordability, and Acceptability (4As) of Telerehabilitation System in Malaysia. Overall development cost of the system is approximately MYR 500. The system enable rehabilitation to be performed at home-setting with a cloud-based monitoring system that will provide long-term monitoring for clinician's assessment. The project provides a proof-of-concept of such system in the Malaysian context.Clinical Relevance - This work demonstrated the proof-of concept of a 4A system is applicable in the Malaysian context. © 2022 IEEE.

Nanotechnology-based healthcare engineering products and recent patents—an update

The past decade has witnessed a tremendous boom in the field of nanotechnology. Owing to several desirable properties of nanomaterials, nanotechnology have revolutionised the current therapeutic paradigm, by carving a niche in the domains of healthcare. Products made by applying the principles of nanotechnology are not only cost-effective from the economic point of view, but also have tremendous advantages like increased specificity, tuneable physico-chemical properties, high surface to volume ratio when compared to their conventional counterparts. In this chapter, a comprehensive portrait of the use of nanotechnology in various aspects of healthcare engineering - ranging from basic research to the fabrication of translational marketed products have been provided. A host of innovative products for application in healthcare including healing body pads, self-heating quilt, smart nano biosensors for health care monitoring, and for diagnosis of several medical conditions have been discussed. This chapter also enlists an array of the recent patents filed for the products made using nano-based approaches in the field of healthcare engineering. The concerns regarding the safe use of nanotechnology-based products for healthcare are also discussed. © 2023 Elsevier Inc. All rights reserved.

Workout Detection by Wearable Device Data Using Machine Learning

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. © 2023 Author(s).

Low-Cost Wearable Device to Provide an Effective Solution for Contact Tracing and Logistics

The paper introduces a low-cost wearable band that does the tedious, repetitive task of entering your required details in any shop or organization, as well as keeping a record of all the people you have come in contact with. There are two aspects of our device:1)If a person enters a shop with our device, the band will transmit the required information of the wearer to the reader kept at the shopkeeper's side wirelessly. The transmitted information will include the wearer's information (as per government guidelines) masked in the band's Unique ID along with their temperature status (whether having a temperature above 100°F or not).2)When two persons come near each other over a distance of 6 feet, the unique ID broadcasted from each other's bands gets stored in the other's band. If any of them tests positive for Coronavirus Disease (COVID-19) or similar diseases, his/her unique ID can be used to trace primary contacts and take appropriate steps to contain further spread.Privacy is key! So, we are reengineering the primary concept of contact tracing and logistics while keeping the user's information safe and secure. © 2023 IEEE.

Tele-Snap: a joint impedance estimation system using snap motor and openPose for remote rehabilitation diagnosis

We propose a remote joint impedance estimation system called Tele-snap for a rehabilitation diagnosis under the COVID-19 pandemic. Dynamic resistance of the human joint is essential physical information reflecting the motor function. The resistance is assessed based on the touching sensation of the doctor (physiotherapist), but the pandemic restricts such an in-person manner. Our proposing system aims to provide this physical information quantified by the joint impedance for a diagnosis in the telerehabilitation context. The proposed system employs a compact impulsive perturbation generator called the snap motor and a marker-less motion capture technology called the OpenPose. The subsystem installed in the patient's place is then simplified remarkably, which consists of the wearable snap motor and Raspberry Pi with a built-in camera module. The proposed system can collect the dataset for impedance estimation through the examiner's teleoperation of the snap motor and camera via a virtual private network, with no need for the operation by the patient. We verify the proposed system through an in-person experiment and then demonstrate the remote impedance estimation scheme. [ FROM AUTHOR] Copyright of Advanced Robotics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Study on technologies implemented in the area of mental health for older adults Post-COVID-19

Mental health has many alterations that are known as mental illnesses or disorders, which affect affective and cognitive processes, mood, thinking and behavior of human beings. Many people suffer from mental health conditions on some occasions. On the other hand, it becomes a mental illness when the symptoms and signs remain, causing stress in the individual, preventing him/her from developing in a natural way. Because of this, this paper proposes an analysis of different information and communication technology (ICT) architectures developed around mental health with the purpose of facilitating the greatest possible help. © 2022 IEEE.

IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives

The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.

Continuous Monitoring for Diagnosis: A Wearable for Covid-19 Detection in Symptomatic Patients

We develop an approach for systematically designing continuous monitoring solutions for early symptom diagnosis. Effective early diagnosis requires collecting and correlating symptoms derived from a number of vitals. For designing a continuous monitoring solution, it is crucial to determine the vitals to be monitored for targeted detection, the errors that can be tolerated, various parameters that need to be tuned, etc. Furthermore, this determination must be made before the design of the monitoring solution itself. Our approach shows how to use a variety of machine learning techniques to systematically derive, tune, and optimize the vitals to be monitored before accessing the continuous monitoring data. We show the effectiveness of our approach in the design of a wearable for early detection of COVID-19 infections in symptomatic patients. © 2023 IEEE.

Identification of design requirements for smart clothes and wearable technology

A design process is introduced to guide the identification of end-user requirements to inform the development of smart clothing with textile-based wearable technologies. The selection of appropriate smart materials and devices may be daunting within a hybrid area that spans the traditionally quite separate sectors of textiles and electronics as they merge within clothing. This chapter looks at requirements capture, guided by a hierarchical ‘process tree', for the development of a clothing layering system to address the design needs of an identified end user, or peer group, for a particular activity or sequence of events, where the function may be enhanced by the application of smart textiles and wearable technologies throughout the garment layers. The process has been revised at a time when the clothing and textile industries are having to give greater consideration to recalibrating their supply chains due to the major disruption caused by the global Covid-19 pandemic. This crisis has put greater emphasis on adopting responsible design solutions, in relation to both environmental and societal challenges associated with the clothing and electronics sectors. It has also accelerated the move of clothing and electronics retail to online transactions that demands greater understanding of the design requirements of the consumer. A design tool is introduced to guide collaborative, cross-disciplinary teams, involved in the development of innovative smart clothing that is both attractive and fit for purpose. © 2023 Elsevier Ltd. All rights reserved.

Design of real-time ECG monitoring wearable equipment based on the Holter system

The COVID-19 epidemic has largely restricted the traditional offline medical treatment model. In this study, we designed ECG monitoring smart clothing based on the Holter system after identifying and analyzing the needs of patients and doctors. This clothing is a wearable device that integrates monitoring and remote diagnosis, building a general network platform to realize remote data transfer sharing and online interactive auxiliary diagnosis. Wearable clothing that can monitor ECG in real time is designed and developed by intelligently integrating limb lead wires, conductive fiber fabrics, lead interfaces, and electrode signal storage receivers by using the human body sensing conduction principle of real-time ECG monitoring. Wearable real-time ECG monitoring clothing can help patients achieve fast virtual medical care and auxiliary diagnosis, and solve the design issues with electrode signal storage receivers. © 2022 IEEE.