Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

Feature selection, construction and test of model for estimating lower extremity strength of older adults using foot motion measured by an in-shoe motion sensor.

Lower extremity strength (LES) is essential to support activities in daily living. To extend healthy life expectancy of elderly people, early detection of LES weakness is important. In this study, we challenge to develop a method for LES assessment in daily living via an in-shoe motion sensor (IMS). To construct the estimation model, we collected data from 62 subjects. We used the outcome of the five-times-sit-to-stand test to represent the performance of LES as the target variable. Predictors were constructed from the subjects' foot motions measured by the IMS during straight path walking. We used the leave-one-subject-out least absolute shrinkage and selection operator algorithm to select features and construct respective models for the males and females. As a result, the models achieved fair and a good intra-class correlation coefficient agreement between the true and estimation values, with mean absolute errors of 2.14 and 1.21 s (variation of 23.6 and 16.0%), respectively. To validate the models, we separately collected data from 45 subjects. The models successfully predicted 100% and 90% of the male and female subjects' data, respectively, which suggests the robustness of the constructed estimation models. The results suggested that LES can be identified more effectively in daily living by wearing an IMS, and the use of an IMS has the potential for future frailty and fall risk assessment applications.

Sub-1.4 cm3 capsule for detecting labile inflammatory biomarkers in situ.

Transient molecules in the gastrointestinal tract such as nitric oxide and hydrogen sulfide are key signals and mediators of inflammation. Owing to their highly reactive nature and extremely short lifetime in the body, these molecules are difficult to detect. Here we develop a miniaturized device that integrates genetically engineered probiotic biosensors with a custom-designed photodetector and readout chip to track these molecules in the gastrointestinal tract. Leveraging the molecular specificity of living sensors1, we genetically encoded bacteria to respond to inflammation-associated molecules by producing luminescence. Low-power electronic readout circuits2 integrated into the device convert the light emitted by the encapsulated bacteria to a wireless signal. We demonstrate in vivo biosensor monitoring in the gastrointestinal tract of small and large animal models and the integration of all components into a sub-1.4 cm3 form factor that is compatible with ingestion and capable of supporting wireless communication. With this device, diseases such as inflammatory bowel disease could be diagnosed earlier than is currently possible, and disease progression could be more accurately tracked. The wireless detection of short-lived, disease-associated molecules with our device could also support timely communication between patients and caregivers, as well as remote personalized care.

Remote Blood Oxygen Estimation From Videos Using Neural Networks.

Peripheral blood oxygen saturation (SpO 2) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO 2 before any obvious symptoms. Measuring an individual's SpO 2 without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO 2 using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone's camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO 2 estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual's hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO 2 measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO 2 measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO 2 estimation performance.

Chip-less wireless electronic skins by remote epitaxial freestanding compound semiconductors.

Recent advances in flexible and stretchable electronics have led to a surge of electronic skin (e-skin)-based health monitoring platforms. Conventional wireless e-skins rely on rigid integrated circuit chips that compromise the overall flexibility and consume considerable power. Chip-less wireless e-skins based on inductor-capacitor resonators are limited to mechanical sensors with low sensitivities. We report a chip-less wireless e-skin based on surface acoustic wave sensors made of freestanding ultrathin single-crystalline piezoelectric gallium nitride membranes. Surface acoustic wave-based e-skin offers highly sensitive, low-power, and long-term sensing of strain, ultraviolet light, and ion concentrations in sweat. We demonstrate weeklong monitoring of pulse. These results present routes to inexpensive and versatile low-power, high-sensitivity platforms for wireless health monitoring devices.

Den-Associated Behavior of Octopus rubescens Revealed by a Motion-Activated Camera Trap System.

Dens are a crucial component of the life history of most shallow water octopuses. However, den usage dynamics have only been explored in a few species over relatively short durations, and Octopus rubescens denning behavior has never been explored in situ. We built four underwater camera traps to observe the behavior of O. rubescens in and around their dens. To distinguish individuals, octopuses were captured and given a unique identifiable visible implant elastomer tag on the dorsal side of their mantle. After being tagged and photographed, each octopus was released back to its original capture site within its original den bottle. The site is unique in that octopuses reside almost exclusively in discarded bottles, therefore aiding in locating and monitoring dens. Motion-activated cameras were suspended in a metal field-of-view above bottle dens of released octopuses to observe den-associated behaviors. Cameras were regularly retrieved and replaced to allow continuous monitoring of den locations in 71 h intervals for over a month. We found that O. rubescenswas primarily active during the day and had frequent interactions with conspecifics (other members within the species). We also found that rockfish and red rock crabs tended to frequent den locations more often when octopuses were not present, while kelp greenling both visited dens more frequently and stayed longer when octopuses were present. Our results, demonstrate the utility of motion-activated camera traps for behavioral and ecological studies of nearshore mobile organisms.

Remote control of the heart and beyond.

A resorbable closed-loop sensor-actuator implant can temporarily control heart rate.

Validation of Inter-Rater and Intra-Rater Reliability of Remote Dielectric Sensing Measurement.

Remote dielectric sensing (ReDS) is a recently introduced non-invasive electromagnetic-based device used to quantify lung fluid levels. Nevertheless, its inter-rater and intra-rater reliability remain uncertain. In 10 healthy volunteers, ReDS values were measured three times successively by the officially trained expert examiner to validate intra-rater reliability. Similar measures were performed by a total of three examiners to validate inter-rater reliability. Intra-class correlation (ICC) was applied to validate each reliability. Ten healthy volunteers [median 34 (32, 40) years old, 10 men, body mass index 23.0 (21.2, 23.9) ] were included. Median ReDS value was 28% (25%, 31%). For the intra-rater reliability, ICC (1, 1) and ICC (1, 3) were 0.966 and 0.988, respectively (P < 0.001). For the inter-rater reliability, ICC (2, 1) and ICC (2, 3) were 0.683 and 0.866, respectively (P < 0.001). Given almost perfect intra-rater reliability, an examiner does not need to repeat ReDS measurement. Given substantial inter-rater reliability, ReDS measurements had better be measured by multiple examiners if possible.

Arrhythmic Burden and the Risk of Cardiovascular Outcomes in Patients With Paroxysmal Atrial Fibrillation and Cardiac Implanted Electronic Devices.

BACKGROUND: Whether the amount of atrial fibrillation (AF) patients experience conveys important prognostic information beyond that provided by the diagnosis of AF is uncertain. The study objective was to assess the dose-response relationship between device-detected AF burden and subsequent cardiovascular outcomes. METHODS: Among patients with paroxysmal AF who underwent cardiac implantable electronic device implantation (2010-2016), Merlin.net remote-monitoring data were linked to Medicare claims to assess the magnitude and strength of the associations between device-based AF burden (defined as a daily percentage of time spent in AF or maximal AF episode duration ascertained at baseline over 30 days) and key cardiovascular outcomes. RESULTS: Among 39 710 patients (mean age 77.1±8.7 years, 60.7% male, and a mean CHA2DS2-VASc score 4.9±1.3), all-cause mortality at 1-year increased with baseline AF burden: 8.54% with AF burden 0%, 8.9% with AF burden 0% to 5%, and 10.9% with AF burden 5% to 98% (P<0.001) There was also a dose-response relationship between increasing AF burden and all-cause or cardiovascular hospitalization and ischemic stroke. Updating AF burden data every 30 days did not alter the AF burden-prognostic relationships determined from the use of baseline data alone. Results were also consistent when 3-year outcomes were considered and after accounting for the use of oral anticoagulants. CONCLUSIONS: In paroxysmal AF, there is a clinically relevant dose-response relationship between increasing AF burden and rates of adverse outcomes at 1- and 3-years, including increasing risks of cardiovascular hospitalization, ischemic stroke, and mortality.

Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey.

In this paper, we have reviewed and presented a critical overview of "energy-efficient and reliable routing solutions" in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing "energy-efficient and reliable routing solutions" for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs.

Forest fire detection system using wireless sensor networks and machine learning.

Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.

Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland.

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.

Implantable devices for heart failure monitoring.

Heart failure (HF) is associated with considerable morbidity and mortality. The increasing prevalence of HF and inpatient HF hospitalization has a considerable burden on healthcare cost and utilization. The recognition that hemodynamic changes in pulmonary artery pressure (PAP) and left atrial pressure precede the signs and symptoms of HF has led to interest in hemodynamic guided HF therapy as an approach to allow earlier intervention during a heart failure decompensation. Remote patient monitoring (RPM) utilizing telecommunication, cardiac implantable electronic device parameters and implantable hemodynamic monitors (IHM) have largely failed to demonstrate favorable outcomes in multicenter trials. However, one positive randomized clinical trial testing the CardioMEMS device (followed by Food and Drug Administration approval) has generated renewed interest in PAP monitoring in the HF population to decrease hospitalization and improve quality of life. The COVID-19 pandemic has also stirred a resurgence in the utilization of telehealth to which RPM using IHM may be complementary. The cost effectiveness of these monitors continues to be a matter of debate. Future iterations of devices aim to be smaller, less burdensome for the patient, less dependent on patient compliance, and less cumbersome for health care providers with the integration of artificial intelligence coupled with sophisticated data management and interpretation tools. Currently, use of IHM may be considered in advanced heart failure patients with the support of structured programs.

The predictive role of early recurrences of atrial arrhythmias after pulmonary vein cryoballoon ablation. Is blanking period an outdated concept? Insights from 12-month continuous cardiac monitoring.

BACKGROUND: Early recurrences of atrial arrhythmias (ERAA) after atrial fibrillation (AF) catheter ablation do not predict procedural failure. A well-demarcated homogeneous lesion delivered by cryoballoon is less arrhythmogenic, and the recommended three-months blanking period may not refer to cryoballoon ablation (CBA). OBJECTIVE: We aimed to evaluate the predictive role of ERAA after second-generation CBA using an implantable loop recorder. METHODS: This prospective observational study enrolled 100 patients (58 males, median age 58) with paroxysmal/persistent AF undergoing pulmonary vein (PV) CBA using second-generation cryoballoon with simultaneous ECG loop recorder implantation. The duration of follow-up was 12 months, with scheduled visits at 3, 6 and 12 months. RESULTS: 99 patients from 100 completed the 12-month follow-up period. ERAA occurred in 31.3 % of patients. 83.9 % of patients with ERAA also developed late recurrences. The 12-month freedom from AF in patients with ERAA was significantly lower than in those without ERAA (p < 0.0001). Non-paroxysmal AF and longer arrhythmia history were associated with increased risk of both early (HR 3.27; 95 % CI 1.32-8.08; p = 0.010 and HR 1.0147; 95 % CI 1.008-1.086; p = 0.015, respectively) and late recurrences (HR 3.89; 95 % CI 1.67-9.04; p = 0.002 and HR 1.0142; 95 % CI 1.007-1.078; p = 0.019, respectively) of AF. ERAA were another predictor for procedural failure (HR 15.2; 95 % CI (6.42-35.99; p = 0.019). CONCLUSIONS: ERAA occurred in the third of the patients after PV second-generation CBA and are strongly associated with procedural failure. Longer duration of AF history and persistent AF are independent predictors of AF's early and late recurrence.

Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat.

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.

Hyperspectral absorption microscopy using photoacoustic remote sensing.

An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide collection of photoacoustic excitation wavelengths ranging from 210 nm to 1550 nm was provided by a nanosecond tunable source allowing access to various salient endogenous chromophores such as DNA, hemeproteins, and lipids. Sensitivity of the device was demonstrated by characterizing the infrared absorption spectrum of water. Meanwhile, the efficacy of the technique was explored by recovering cell nuclei and oxygen saturation from a live chicken embryo model and by recovering adipocytes from freshly resected murine adipose tissue. This represents a continued investigation into the characteristics of the hyperspectral photoacoustic remote sensing technique which may represent an effective means of non-destructive endogenous contrast characterization and visualization.

Digital health device measured sleep duration and ideal cardiovascular health: an observational study.

BACKGROUND: Studies relying on self-reported sleep data suggest that there is an association between short and long sleep duration and less than ideal cardiovascular health. Evidence regarding the feasibility of using digital health devices to measure sleep duration and assess its relationship to ideal cardiovascular health are lacking. The objective of the present study was to utilize digital health devices to record sleep duration and examine the relationship between sleep duration and ideal cardiovascular health. METHODS: A total of 307 participants transmitted sleep duration data from digital health devices and answered the Life's Simple 7 survey instrument to assess ideal cardiovascular health. Sleep duration was defined as adequate (7 to < 9 h per night) or non-adequate (< 7 h and ≥ 9 h). RESULTS: We identified three sleep-cardiovascular health phenogroups: resilient (non-adequate sleep and ideal cardiovascular health), uncoupled (adequate sleep and non-ideal cardiovascular health) or concordant (sleep and cardiovascular health metrics were aligned). Participants in the resilient phenogroup (n = 83) had better cardiovascular health factor profiles (blood pressure, blood glucose and cholesterol levels) and behaviors (healthy weight, diet, exercise, smoking) than participants in the concordant (n = 171) and uncoupled (n = 53) phenogroups. This was associated with higher Life's Simple 7 Health Scores in the resilient phenogroup compared to the concordant and uncoupled phenogroups (7.8 ± 0.8 vs. 7.0 ± 1.4 vs. 5.6 ± 0.7, P < 0.01). CONCLUSION: This study identified three distinct sleep-ideal cardiovascular health phenogroups and highlights the advantage of incorporating sleep assessments into studies of cardiovascular health. Future studies should focus on the relationship between sleep-cardiovascular phenogroups and clinical outcomes. Clinical Trial Registration Clinicaltrials.gov NCT02958098. Date of registration: November 11, 2016.

Diagnosis of pine wilt disease using remote wireless sensing.

Pine wilt disease caused by Bursaphelenchus xylophilus is a major tree disease that threatens pine forests worldwide. To diagnose this disease, we developed battery-powered remote sensing devices capable of long-range (LoRa) communication and installed them in pine trees (Pinus densiflora) in Gyeongju and Ulsan, South Korea. Upon analyzing the collected tree sensing signals, which represented stem resistance, we found that the mean absolute deviation (MAD) of the sensing signals was useful for distinguishing between uninfected and infected trees. The MAD of infected trees was greater than that of uninfected trees from August of the year, and in the two-dimensional plane, consisting of the MAD value in July and that in October, the infected and uninfected trees were separated by the first-order boundary line generated using linear discriminant analysis. It was also observed that wood moisture content and precipitation affected MAD. This is the first study to diagnose pine wilt disease using remote sensors attached to trees.

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds.

Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs' feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs' feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model's accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12-27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs' seagrass habitat use.