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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.
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The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE
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About 80% of the patients recovering from COVID-19 have inflammation symptoms, like brain fog, myopathy, myalgia, muscle weariness, headache, mental tiredness, asthenia, adynamia, dizziness, tinnitus, hearing loss, telogenic effluvium and mood disturbances. Here, we demonstrate how transcranial and systemic photobiomodulation using near-infrared LEDs emitting 850 nm wavelength light enhanced cognition and reduced pain. Participants were separated into transcranial photobiomodulation with near-infrared LEDs (850 nm, 10W, 10 minutes), photobiomodulation with a punctual cutaneous application (850nm, 10W, 10-40 minutes), and both treatments. All patients underwent 10-day treatments at least. © 2023 SPIE.
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BACKGROUND: Oxidative stress (OS) could cause various COVID-19 complications. Recently, we have developed the Pouvoir AntiOxydant Total (PAOT®) technology for reflecting the total antioxidant capacity (TAC) of biological samples. We aimed to investigate systemic oxidative stress status (OSS) and to evaluate the utility of PAOT® for assessing TAC during the recovery phase in critical COVID-19 patients in a rehabilitation facility. MATERIALS AND METHODS: In a total of 12 critical COVID-19 patients in rehabilitation, 19 plasma OSS biomarkers were measured: antioxidants, TAC, trace elements, oxidative damage to lipids, and inflammatory biomarkers. TAC level was measured in plasma, saliva, skin, and urine, using PAOT and expressed as PAOT-Plasma, -Saliva, -Skin, and -Urine scores, respectively. Plasma OSS biomarker levels were compared with levels from previous studies on hospitalized COVID-19 patients and with the reference population. Correlations between four PAOT scores and plasma OSS biomarker levels were analyzed. RESULTS: During the recovery phase, plasma levels in antioxidants (γ-tocopherol, ß-carotene, total glutathione, vitamin C and thiol proteins) were significantly lower than reference intervals, whereas total hydroperoxides and myeloperoxidase (a marker of inflammation) were significantly higher. Copper negatively correlated with total hydroperoxides (r = 0.95, p = 0.001). A similar, deeply modified OSS was already observed in COVID-19 patients hospitalized in an intensive care unit. TAC evaluated in saliva, urine, and skin correlated negatively with copper and with plasma total hydroperoxides. To conclude, the systemic OSS, determined using a large number of biomarkers, was always significantly increased in cured COVID-19 patients during their recovery phase. The less costly evaluation of TAC using an electrochemical method could potentially represent a good alternative to the individual analysis of biomarkers linked to pro-oxidants.
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All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.
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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.
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As the world is entering its 3rd year of the COVID-19 pandemic, the number of COVID-19 patients are increasing. So as the number of post-COVID patients who need rehabilitation. This paper proposes a web-based telerehabilitation system with the aims to aid COVID-19 rehabilitation research and clinical trial management. Our proposed system allows researchers to conduct various experiments such as physical therapeutic treatment and herbal treatment on COVID-19 outpatients. The web-based system is chosen for its ubiquity and cost effectiveness where patients can easily participate in the rehabilitation program remotely from any tablet devices. Stakeholder involvement is crucial to the long-term success of this work. Therefore, user experience methodology is used to gain user adoption at the beginning of the project. Initial testing has shown satisfactory results. The developed system is expected to be used in an actual rehabilitation research. © 2022 IEEE.
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The Coronavirus crisis has forced hospitals globally to develop new virtual service portals and systems to: 1) triage, diagnose, and manage new patients virtually for every clinical specialty at home as their symptoms emerge, avoiding COVID-19 exposure to patient or physicians, and hospitalization, as much as possible, and 2) discharge, track, and support recovered patients via homecare and virtual visits to free as many critical care beds as possible. This paper focuses on simulating and modeling an episode of care with innovative initial patient contact and triage processes using the Colored Petri Net (CPN) formalism to help optimize workflow, patient throughput, and overall system efficacy. The two patient triage programs under consideration are a health system in Australia and an orthopedic surgical program in the US. We describe our model for the US program. Our presented results establish a desired stratification of patients through a virtual musculoskeletal triage. © 2022 IEEE Computer Society. All rights reserved.
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The pandemic turned life upside down, including causing unavailability and an inability to access rehabilitation in the hospital. However, the need to be fit and healed does not stop, so rehabilitation innovation from the digital sectors plays a role in approaching the patient, as the patient requires a medical professional to be healed. Rehabilitation via a digital pathway is fraught with difficulties, but advances in technology and research have enabled it to be used to the greatest extent possible in this disaster. Digital health has increased its efficacy in response to the pandemic, as it is now available in developing countries where there is an inability to visit a clinic for rehabilitation, and now the rehabilitation tool is accessible to the patients in their hands and they can connect to their therapist at any time. The rehabilitation is designed based on the patient's illness, feedback, and health data stored on the application devices, which regulate and provide feedback from both sides, from the patient and other improvement changes gathered with the help of digital applications. Digital health allows for online consultation, assessment, and 24-h monitoring, all of which are directly shared with the rehabilitation team. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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A fall in third age triggers a domino effect of consequences that are recognized by specialists as leading causes of further falls. After the first event, the post-fall syndrome onsets: a pathological fear of falling that affects quality of life. It leads to loss of self-efficacy, sedentarism, musculoskeletal weakening, reduced mobility, postural insufficiency, gait disorders, isolation and depression—all acknowledged as fall risk factors. Specialists agreed that the most effective approach to prevent new episodes is to restore confident postures and good alignments. This paper presents the first design stages of a soft-actuated re-educational garment for remote post-fall rehabilitation in female users. The objective is to i) restore postural control by providing a gentle pressure stimulus, suggesting corrections when poor body alignments are detected;ii) restore the perceived self-efficacy;iii) promote physical activity by motion monitoring and providing daily reports through a patient-therapist smartphone app. To date, we have tested a soft body-postures detection system by cross-checking data from a network of e-textile stretch sensors, along with a pneumatic actuator system around the user's torso providing a targeted pressure stimulus to correct bad habits. Tests have been run on a limited number of users due to the Covid-19 emergency. Data are not yet statistically conclusive but suggest the way to a new dimensional approach, both for rehabilitation and prevention. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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Developing countries like Nepal face challenges in accessing health services due to sparse distribution in communities, difficult geographic terrain, limited transportation, poverty, and lack of health human expertise in rural areas. The COVID-19 pandemic added woes to the wound. To address this gap, the Hospital for Children, Eye, ENT, and Rehabilitation Services adopted an innovative approach to remote rural patient care using telehealth and artificial intelligence in close coordination with IT professionals and healthcare professionals. We developed a deep learning-based disease prediction model that incorporates telemedicine with AI for screening and diagnosing Eye and ENT diseases using nonspecialist health workers. Deep learning-based disease prediction models in Diabetic Retinopathy (DR) and Glaucoma added quality specialized services to telehealth. This paper presents the adoption of digital innovations and the incorporation of telehealth to tackle various diseases. To predict DR, 61,458 colorful retinal photographs from fundus photography and 1500 for Glaucoma were used. To reduce the biases, EyePACS data sets were also incorporated. Inception V3 transfer learning model was used for DR and employed DenseNet architecture for Glaucoma. An accuracy of more than 90 %in both models was achieved. Accurate specialized diagnosis, better medical care, patient monitoring, limited specialized hospital visits, and easier with shorter wait times are now possible. In the future, this successful model can be replicated nationally and in other developing countries. © 2022 IEEE.
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This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients' data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Hand rehabilitation has been widely studied since it affects the life quality and independence of those affected. Hand impairment can be caused by several conditions, among them strokes and other cerebrovascular accidents, affecting the capabilities of those who survive them in performing the activities of daily living (ADL). Rehabilitation seeks to restore the ability of a person to perform these crucial ADL. There is a current trend in using robotic rehabilitation and other industry 4.0 tools since it can provide a safe, intensive, and task-oriented at a relatively low cost, which can be combined with other technologies such as virtual and augmented reality, BCI, haptics, and others. Moreover, it can provide accessibility in the face of current panoramas such as COVID-19. Hand exoskeleton robots are one of the most extended robotic devices for rehabilitation. However, a design adapted to the patient's needs is necessary to achieve their capability fully and succeed in rehabilitation. One of the main challenges is that several considerations and parameters affect these devices' design and the broad approaches that can be followed. This brief review aims to understand and empathize as a source of inspiration during the design process of hand exoskeleton robots for rehabilitation. © 2022 IEEE.
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Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model –soft expert sets– deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets. N-soft expert sets and their fuzzy version, namely, fuzzy N-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the N-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of N-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy N-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility. © 2023 The Author(s)
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), may lead to thyroid disorders, including both thyrotoxicosis and suppression of thyroid function. The aim of the present study was to assess the post-COVID-19 effects on thyroid function in patients without history of thyroid disease after complete recovery from mild-to-moderate COVID-19. Thyroid function tests [thyroid-stimulating hormone (TSH), free thyroxine (fT4), antithyroid antibodies] were performed on 113 patients (median age of 43.0 years;31.0% male) two months after initial SARS-CoV-2 infection. TSH and fT4 were determined again one month later in this observational, prospective study. Thyroid dysfunction was registered in 61.1% of the patients (78.3% subclinical hypothyroidism, 13% subclinical hyperthyroidism and 8.7% overt hypothyroidism) two months after COVID-19. Moderate rather than mild manifestation of COVID-19 was significantly associated with a higher risk of thyroid dysfunction (OR 5.33;95% CI: 1.70–16.69, p = 0.002), presence of thyroglobulin antibodies and need for levothyroxine therapy. At the follow-up, the subclinical hypothyroidism persisted in 28.3% of the subjects. Moreover, the TSH level was significantly reduced in comparison to the second month after the initial COVID-19 infection in all the patients (p < 0.001), but not in those with subclinical hypothyroidism and without hormone replacement therapy. Our findings indicate that COVID-19 could have long-term, negative effects on thyroid function. Therefore, thyroid function testing should be included in the follow-up algorithm of COVID-19 survivors. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Due to the coronavirus pandemic, portable electrical impedance tomography (EIT) systems [1]-[3] have been considered as the only variable wearable medical lung imaging solution for monitoring the treatment of pneumonia patients and their recovery. Generally, the EIT system is classified into passive EIT (P-EIT) [3]-[6] or active electrode EIT (AE-EIT) [2]. The AE-EIT system is preferred as it amplifies and digitalizes the small signals while minimizing the noises incurred by motion artifacts, complex long wire connection, large variation in electrode contact, and stray capacitance problems, which is important for high-performance imaging applications. © 2022 IEEE.
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The novel corona virus (COVID-19), was initially seen in some cities of China in Dec 2019 and then spread exponentially in the entire world and converted into the worldwide pandemic. It rapidly influences and affect day to day life of everybody and slow down economy maximum countries. An immediate requirement raised to detect the positive cases on starting stage and some method to stop further spread. Radiology images have played very important role for detecting COVID-19 and it was found that these images contain very important data which is very much effective in proper diagnosis and treatment. This all creates a requirement of machine learning based artificial intelligent system to detect and further treatment of COVID-19 using X-Ray and CT images and other similar data available. Machine learning based artificial intelligent system can assist and big help for medical staff during diagnoses of COVID-19. This will also be very helpful and fill the gap of shortage of medical staff in interior towns worldwide. As we have seen that COVID-19 virus spread so fast and impact millions of patients in very short time. This creates the requirement of some computerized system that will help in diagnoses and speedy recovery of patients. One another main test which people were using was RT-PCR for detection of COVID-19 but because of many false negative results and time taken in process we need one customized Machine learning based artificial intelligent system that makes use CT images. The proposed system COVID-Rational (COVID-R) is really helpful for early detection of COVID-19 by using classification technique with supervised learning algorithms like random forest and support vector machine (SVM). We have achieved good performance assessment with accuracy of 90.2% for early detection of COVID-19 with our proposed system COVID-R. © 2022 IEEE.
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Bipolar disorder (BD) is a highly pathological disorder that is often misdiagnosed or undiagnosed. The main treatment is a combination of psychotherapy and medication. Traditional psychotherapy is affected by factors such as time, space, shortage of professional psychotherapists and patients' stigma, and has low availability. In terms of drug therapy, patients' medication compliance is poor, leading to repeated illness. The epidemic of coronavirus in 2019, closed management and telemedicine provide new ideas for the treatment of BD. Telemedicine can provide convenient medical services, promote disease rehabilitation, effectively guide patients' self-management, improve patients' treatment compliance, and prevent disease recurrence. This paper analyzes and summarizes the research related to telemedicine in BD, including the origin and significance, technical methods and application effects. To provide a reference for the application of telemedicine in patients with BD. © 2022 ACM.
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Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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Virtual Reality systems take an important place in training and rehabilitation of children with psychological problems. This paper proposes developing an Immersive Virtual Reality Training System in Unity software to enrich social interaction and communication skills for children with Autism Spectrum Disorder. Individuals with ASD have trouble with social interaction, adaptation to new surroundings and communication. In this system the behavior of the virtual trainer changes depending on the mood and actions of the patients. The face and speech recognition uses an integration of Artificial Intelligent algorithms with Virtual Reality in Unity software which is implemented in C# programming language. Virtual characters and objects of the training system are designed in Autodesk Maya software. Integration with Virtual Reality carried out using wireless VR headset Oculus Quest2. Earlier research works found that applications with virtual reality have the potential to provide useful clinical treatments for people with autism. The proposed VR Training system will be an innovative solution for the rehabilitation of children with Autism Spectrum Disorder. This system is offered to users with a high degree of interactivity and practicing various social situations in a safe and controlled environment. With the start of the COVID-19 pandemic, the integration of virtual reality training systems has become extremely important. The use of such high-tech solutions allows patients who need the treatment to receive therapy without leaving home. This development of the Immersive Virtual Reality Training System is the first experience of treating children with autism in Kazakhstan. © 2022 IEEE.