A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999PEuropean Union (EU) MINECO/FEDER MAT2017-85999PRegional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-1

Current Challenges and Barriers to the Wider Adoption of Wearable Sensor Applications and Internet-of-Things in Health and Well-being

The aim of this review is to investigate barriers and challenges of Wearable Sensors (WS) and Internet-of-Things (IoT) solutions in healthcare. This work specifically focuses on falls and Activity of Daily Life (ADLs) for ageing population and independent living for older adults. The majority of the studies focussed on the system aspects of WS and IoT solutions including advanced sensors, wireless data collection, communication platforms and usability. The current studies are focused on a single use-case/health area using non-scalable and ‘silo’ solutions. Moderate to low usability/ userfriendly approach is reported in most of the current studies. Other issues found were, inaccurate sensors, battery/power issues, restricting the users within the monitoring area/space and lack of interoperability. The advancement of wearable technology and possibilities of using advanced technology to support ageing population is a concept that has been investigated by many studies. We believe, WS and IoT monitoring plays a critical role towards support of a world-wide goal of tackling ageing population and efficient independent living. Consequently, in this study we focus on identifying three main challenges regarding data collection and processing, techniques for risk assessment, usability and acceptability of WS and IoT in wider healthcare settings

Smart technologies and beyond: exploring how a smart band can assist in monitoring children’s independent mobility & well-being

The problem which is being investigated through this thesis is not having a device(s) or method(s) which are appropriate for monitoring a child’s vital and tracking a child’s location. This aspect is being explored by other researchers which are yet to find a viable solution. This work focuses on providing a solution that would consider using the Internet of Things for measuring and improving children’s health. Additionally, the focus of this research is on the use of technology for health and the needs of parents who are concerned about their child’s physical health and well-being. This work also provides an insight into how technology is used during the pandemic. This thesis will be based on a mixture of quantitative and qualitative research, which will have been used to review the following areas covering key aspects and focuses of this study which are (i) Children’s Independent Mobility (ii) Physical activity for children (iii) Emotions of a child (iv) Smart Technologies and (v) Children’s smart wearables. This will allow a review of the problem in detail and how technology can help the health sector, especially for children. The deliverable of this study is to recommend a suitable smart band device that enables location tracking of the child, activity tracking as well as monitoring the health and wellbeing of the child. The research also includes an element of practical research in the form of (i) Surveys, the use of smart technology and a perspective on the solution from parents. (ii) Focus group, in the form of a survey allowing opinions and collection of information on the child and what the parents think of smart technology and how it could potentially help with their fears. (iii) Observation, which allows the collection of data from children who were given six activities to conduct while wearing the Fitbit Charge HR. The information gained from these elements will help provide guidelines for a proposed solution. In this thesis, there are three frameworks which are about (i) Research process for this study (ii) Key Performance Indicators (KPIs) which are findings from the literature review and (iii) Proposed framework for the solution, all three combined frameworks can help health professionals and many parents who want an efficient and reliable device, also deployment of technologies used in the health industry for children in support of independent mobility. Current frameworks have some considerations within the technology and medical field but were not up to date with the latest elements such as parents fears within today’s world and the advanced features of technology

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Development of a clinician-facing prototype for health monitoring using smartwatch data

Wearable technology in the form of smartwatches and advanced analytics are set to reshape healthcare by facilitating prevention, early diagnosis, personalized treatment, and management of chronic diseases. However, gaining insights from vast amounts of smartwatch data remains challenging for healthcare providers due to complex data formats and lack of training. To mitigate this shortcoming, healthcare professionals need tools that support them in analysing and presenting smartwatch data in an easy-to-understand way. Therefore, this study uses a design science approach to co-design, develop and evaluate an application prototype that analyses smartwatch data and allows healthcare providers to use this data to manage aspects of patients\u27 health. The preliminary results of the co-design and development phases are reported. The meaningful involvement of healthcare providers in designing and evaluating such analytical tools would help develop relevant and useful health monitoring applications that can be scaled in real-world settings

Wearable and portable GPS solutions for monitoring mobility in dementia: A systematic review

Dementia is the most common neurodegenerative disorder globally. Disease progression is marked by declining cognitive function accompanied by changes in mobility. Increased sedentary behaviour and, conversely, wandering and becoming lost are common. Global positioning system (GPS) solutions are increasingly used by caregivers to locate missing people with dementia (PwD) but also offer a non-invasive means of monitoring mobility patterns in PwD. We performed a systematic search across five databases to identify papers published since 2000, where wearable or portable GPS was used to monitor mobility in patients with common dementias or mild cognitive impairment (MCI). Disease and GPS-specific vocabulary were searched singly, and then in combination, identifying 3004 papers. Following deduplication, we screened 1972 papers and retained 17 studies after a full-text review. Only 1/17 studies used a wrist-worn GPS solution, while all others were variously located on the patient. We characterised the studies using a conceptual framework, finding marked heterogeneity in the number and complexity of reported GPS-derived mobility outcomes. Duration was the most frequently reported category of mobility reported (15/17), followed by out of home (14/17), and stop and trajectory (both 10/17). Future research would benefit from greater standardisation and harmonisation of reporting which would enable GPS-derived measures of mobility to be incorporated more robustly into clinical trials

Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients’ Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r2, 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life—through objective and remote digital outcomes—paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials

THE APPLICATION OF SMARTWATCH IN MANAGING EMPLOYEE HEALTH MONITORING

ABSTRACTWorkplace health issues have continued to increase, and this has caused problems such as increasing medical cost and medical leaves. In response to these issues, employers are starting to adopt health technology to overcome the problem such as smartwatch. Smartwatch technology is a wrist-worn device provided with a variety of sensors that are available for collecting physical activity and location data in real time. This paper aims to explore the future drivers of the smartwatch application in monitoring and managing employees’ health. The research study used exploratory research design utilizing the foresight methods. STEEPV analysis was used to identify the key drivers of smartwatch application and to develop a descriptive survey for assessing the impact and uncertainty of each driver. The survey was distributed to human resources managers of medium-sized companies in Malaysia. Technology readiness of smart watch adoption was evaluated using Technology Readiness Index (TRI). Thirty-five respondents took part in online survey. From the data analysis, top two drivers had been identified which are “social interaction” and “data transparency”. These drivers were used for developing future scenario of the smartwatch application in monitoring and managing employee health in the next 5 to 10 years. Four scenarios had been discussed in this paper which are healthy workplace environment, unattainable technology adoption, inefficient technology, and low adoption of smartwatch. This research would provide additional information about the future scenario of smartwatch application in managing employee health monitoring in Malaysia. Keywords: Smartwatch; Employee Health Monitoring; Technological Readines

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions. In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio