Underactuated digital twin's robotic hands with tactile sensing capabilities for well-being

Humans are social by nature, and the current physical distancing due to COVID has converted many of our daily interactions into virtual ones. Among the negative consequences of this, we find the lack of an element that is essential to humans' well-being, which is the physical touch. We explore the Digital Twin technology's prospect to support in reducing the impact of this on humans. We provide a definition of the concept of Robo Twin and explain its role in mediating the human interactions. Besides, we survey research works related Digital Twin's physical representation with a focus on underactuated Digital Twin's robotic hands. In this survey, we aim at providing findings from literature, to support researchers' decisions in the adoption and use of existing designs and implementations of Digital Twin's robotic hands, and to inform future research on current challenges and gaps in existing research works. © 2023 Elsevier Inc. All rights reserved.

Smart Homecare Research Translation into Broader Practice: Enablers, Barriers and Directions

Smart homecare utilises advanced technologies to support, improve and promote remote healthcare in homes and communities through collecting and analysing health data and sharing this knowledge with carers and clinicians. With the continuous growth in the world’s older population, smart homecare becomes increasingly crucial in providing in-home care for older adults, allowing the vital healthcare dollars to go further into other critical care needs. In addition, with the rise in the development and utilisation of innovative technologies in healthcare settings, it is vital to ensure that these technologies are guided and approved by the corresponding regulatory bodies such as FDA (Foods and Drug Administration) in the USA and TGA (Therapeutic Good Administration) in Australia. With this premise, this paper identifies four dimensions for researchers to consider when developing smart homecare solutions for in-home remote care: Technology, Data, People, and Operational Environment. The essential interplays amongst these four dimensions are discussed to identify the various enablers and barriers in the successful delivery of smart homecare solutions. As the primary output of this paper, it proposes a conceptual framework to achieve practical in-home care for the older population living independently with the support of technology, while addressing the challenges such as security and privacy of patient data. Secondly, a comprehensive and practical guide featuring seven phases is presented to support and direct researchers in implementing smart homecare solutions for remote care. The proposed framework and the guide aim to make smart homecare research practical and truly translational into broader practice. Author

The role of IOT in transforming community pharmacy

During Covid-19 Pandemic the entire world experiences the role and importance of Internet of Things (IoT) in healthcare system and especially in Community Pharmacy. Internet of Things can be described as a network architecture incorporating an abundance of sensors software hardware, computing devices, technologies, machines and many more utilities, assisting us in our relevant domains as per the needful. Over the yesteryears it has been observed that the footprints of Internet of Things over the healthcare sector have increased eloquently. This observation leads us to the fact that with the escalating population and the healthcare plight Internet of Things can really prove itself to be serious breakthrough in the community pharmacy. By revising the current healthcare system in the community pharmacies in a more socio-economic fashion. This paper reviews the technological advancements in Internet of Things, for the medical management of public health so that it can be vitally made use of in the domain of community pharmacy. There is a demand of efficient Internet of Things framework that would seriously be able to challenge the abysmal state of community pharmacy mostly in the remote areas and the distant regions. © 2022 MEDIC SCIENTIFIC. All Rights Reserved.

Can Remote Patient Monitoring Be the New Standard in Primary Care of Chronic Diseases, Post-COVID-19?

Background: One lesson from the current COVID-19 pandemic is the need to optimize health care provision outside of traditional settings, and potentially over longer periods of time. An important strategy is remote patient monitoring (RPM), allowing patients to remain at home, while they transmit health data and receive follow-up services. Materials and Methods: We conducted an overview of the latest systematic reviews that had included randomized controlled trials with adult patients with chronic diseases. We summarized results and displayed these in forest plots, and used GRADE (Grading of Recommendations Assessment, Development, and Evaluation) to assess our certainty of the evidence. Results: We included 4 systematic reviews that together reported on 11 trials that met our definition of RPM, each including patients with diabetes and/or hypertension. RPM probably makes little to no difference on HbA1c levels. RPM probably leads to a slight reduction in systolic blood pressure, with questionable clinical meaningfulness. RPM probably has a small negative effect on the physical component of health-related quality of life, but the clinical significance of this reduction is uncertain. We have low confidence in the finding that RPM makes no difference to the remaining five primary outcomes. Conclusion: Most of our findings are consistent with reviews of other, broader definitions of RPM. The type of RPM examined in this review is as effective as standard treatment for patients with diabetes/hypertension. If this or other types of RPM are to be used for "long covid" patients or for other chronic disease groups post-pandemic, we need to understand why RPM may negatively affect quality of life.

Rapid Adoption of Telemedicine Along with Emergent Use of Continuous Glucose Monitors in the Ambulatory Care of Young Persons with New-Onset Type 1 Diabetes in the Time of COVID-19: A Case Series.

Aims: The COVID-19 pandemic has caused strain on hospital systems and potential delay in diagnosis of type 1 diabetes (T1D). Outpatient diagnosis and treatment of metabolically stable young persons with new-onset T1D have been shown to be equivalent to inpatient. We describe an approach to outpatient management of newly diagnosed T1D during the COVID-19 pandemic using an interdisciplinary team, telemedicine, and diabetes technologies including rapid implementation of continuous glucose monitoring (CGM). Methods: Following the onset of the COVID-19 pandemic, new-onset cases of T1D were tracked. After laboratory confirmation of diagnosis and metabolic stability, patients and families were referred for ambulatory initiation of insulin therapy and diabetes education. These cases were reviewed using data extracted from the electronic health record, comments from multidisciplinary team members, and cloud-based glucose data. Results: We report on seven young people with new-onset T1D without diabetic ketoacidosis from April to June 2020, during the COVID-19 pandemic. Ages ranged 9-23 years with presenting hemoglobin A1c (HbA1c) values 10-14.5%. Initial evaluation was generally face-to-face, followed by frequent telemedicine visits. Five patients had a family history of T1D. Two patients had access to at-home HbA1c kits prompting evaluation in the absence of symptoms. Four patients required emergency department evaluation. Five patients presented with ketosis. All patients were prescribed CGM at the first visit, most starting within 1 month. Conclusions: Technology is extraordinarily useful for the care of young persons with new-onset T1D in the ambulatory setting during the COVID-19 pandemic. Large observational studies are needed to better understand outcomes of an outpatient, technology-focused approach.

DairyCare 'blueprint for action': husbandry for wellbeing.

'Keep calm and carry on' was a wartime message to the British public that has achieved renewed fame in the last few years. The strategy was simple: in times of extreme difficulty a cool head combined with stoicism is an appropriate response to ensure a successful outcome. The latest major challenge to society (COVID-19) met with a very different response, and only history will reveal whether 'Stay home and worry' will be equally effective. In devising blueprints or strategies it is extremely important to have a clear idea of what you are trying to achieve, whether it be maintaining world freedom or stopping a pandemic. In the case of livestock agriculture, it is helping to feed a rapidly growing global population in harmony with the needs of current and future generations. I hope that I have stated this clearly, and calmly. If so, I ask you to picture a scene. We are on a Calm Farm. Dairy animals go about their daily lives contented, unhurried and focused on the simple feeding and socialising activities that are so important to them. Unstressed, their productive capacities and abilities to avoid and, when necessary, cope with physiological and pathological challenges are maximised. They are not alone: the exact same characteristics also apply to the farmer and husbandry staff that we meet. How is this calm farming approach relevant to the aspirations we had when we established the EU COST Action DairyCare? Our objective was to harness the power of computing technologies to assist our management of dairy livestock. A simple rearrangement leads us to Computing Assisted Livestock Management, CALM. In this short Research Reflection I shall assess how far we have come towards the achievement of sensible goals related to technological assessment of dairy animal wellbeing, and speculate on what more things both can and need to be done to finish the job. It is a personal account. DairyCare was a major collaboration involving several hundred active researchers. To involve them all would be impossible, and I do not pretend to speak for them all. As will become evident, the wide skills base that was assembled was so successful in its primary objectives that different skills, chiefly in economics, are now needed to exploit all of the technological advance that has been achieved. DairyCare succeeded in a second direction. Whilst the focus was technology development, by assembling a large cohort of biologists with animal welfare interests, it soon became apparent that technology should run alongside and help to enable improved management practices. This Special Issue is, therefore, in two sections. The first is dedicated to technology development and the second to a novel management practice that has the potential to significantly improve the wellbeing of cows and calves: cow-calf contact rearing. That section is introduced by my DairyCare colleague, Sigrid Agenäs.

Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds.

Telemedicine could be a key to control the world-wide disruptive and spreading novel coronavirus disease (COVID-19) pandemic. The COVID-19 virus directly targets the lungs, leading to pneumonia-like symptoms and shortness of breath with life-threatening consequences. Despite the fact that self-quarantine and social distancing are indispensable during the pandemic, the procedure for testing COVID-19 contraction is conventionally available through nasal swabs, saliva test kits, and blood work at healthcare settings. Therefore, devising personalized self-testing kits for COVID-19 virus and other similar viruses is heavily admired. Many e-health initiatives have been made possible by the advent of smartphones with embedded software, hardware, high-performance computing, and connectivity capabilities. A careful review of breathing sounds and their implications in identifying breathing complications suggests that the breathing sounds of COVID-19 contracted users may reveal certain acoustic signal patterns, which is worth investigating. To this end, acquiring respiratory data solely from breathing sounds fed to the smartphone's microphone strikes as a very appealing resolution. The acquired breathing sounds can be analyzed using advanced signal processing and analysis in tandem with new deep/machine learning and pattern recognition techniques to separate the breathing phases, estimate the lung volume, oxygenation, and to further classify the breathing data input into healthy or unhealthy cases. The ideas presented have the potential to be deployed as self-test breathing monitoring apps for the ongoing global COVID-19 pandemic, where users can check their breathing sound pattern frequently through the app.