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.

Utilização de sensores inerciais no teste timed up and go: uma revisão narrativa / Use of inertial sensors in the timed up and go test: a narrative review

O teste funcional Timed Up and Go (TUG) é amplamente utilizado para avaliar o risco de queda, através do equilíbrio e mobilidade, por ser de fácil aplicação e boa reprodutibilidade na prática clínica. Porém, o TUG ainda possui algumas limitações, pois se concentra no tempo total em que o teste é realizado. Uma proposta de avaliação é através da utilização de sensores inerciais, baseados na tecnologia de sistemas microeletromecânicos, e vem sendo muito utilizados para análise do movimento humano. Logo, o objetivo desse estudo foi realizar uma revisão narrativa sobre o uso dos sensores inerciais nas medidas temporais e cinemáticas do TUG e suas subfases. Metodologia: Essa revisão narrativa foi realizada nas bases de dados PubMed, CENTRAL, BVS e PEDro, por meio do vocabulário MeSH entre o período de maio a junho de 2020. Os critérios de inclusão foram estudos que utilizaram sensores inerciais para avaliação de medidas temporais e cinemáticas do TUG e suas subfases. Resultados: Foram incluídos 11 artigos de um total de 2305 achados. Desses, 5 utilizaram os sensores de smartphones. Não houve padronização em relação à quantidade utilizada, nem à fixação e posicionamento. Os sensores conseguiram mostrar diferenças no TUG e suas subfases nas medidas temporais e cinemáticas nos diferentes grupos avaliados. Considerações Finais: Sensores inerciais são capazes de avaliar medidas temporais e cinemáticas do TUG e de suas subfases, mostrando serem ferramentas confiáveis. Entretanto, mesmo obtendo resultados satisfatórios, necessita-se de mais estudos abrangendo uma população maior.

The Timed Up and Go (TUG) functional test is widely used to assess the risk of falling through balance and mobility since it is easy to apply and presents good reproducibility in clinical practice. However, the TUG test still has some limitations, as it focuses on the total time the test is performed. A proposal for evaluation is the use of inertial sensors, based on the microelectromechanical system technology, which has been widely used for the analysis of human movement. Therefore, the objective of this study was to carry out a narrative review on the use of inertial sensors in the temporal and kinematic measurements of TUG and its subphases. Methodology: This narrative review was carried out in the PubMed, CENTRAL, BVS, and PEDro databases using the MeSH vocabulary between the period of May to June 2020. The inclusion criteria were studies using inertial sensors to evaluate temporal and kinematic measurements of the TUG and its subphases. Results: A total of 11 articles were selected from 2305 hits. From these, five (5) used smartphone sensors. There was no standardization regarding the quantity used, nor their fixation and positioning. The sensors were able to show differences in the TUG and its subphases in the temporal and kinematic measurements in the different groups evaluated. Final Considerations: Inertial sensors are capable of evaluating temporal and kinematic measurements of the TUG and its subphases, showing that they are reliable tools. Nevertheless, although satisfactory results were obtained, further studies are needed covering a larger population.

High-integrity human intervention in ecosystems: Tracking self-organization modes.

Humans play major roles in shaping and transforming the ecology of Earth. Unlike natural drivers of ecosystem change, which are erratic and unpredictable, human intervention in ecosystems generally involves planning and management, but often results in detrimental outcomes. Using model studies and aerial-image analysis, we argue that the design of a successful human intervention form calls for the identification of the self-organization modes that drive ecosystem change, and for studying their dynamics. We demonstrate this approach with two examples: grazing management in drought-prone ecosystems, and rehabilitation of degraded vegetation by water harvesting. We show that grazing can increase the resilience to droughts, rather than imposing an additional stress, if managed in a spatially non-uniform manner, and that fragmental restoration along contour bunds is more resilient than the common practice of continuous restoration in vegetation stripes. We conclude by discussing the need for additional studies of self-organization modes and their dynamics.

Association of kidney function and atrial fibrillation progression to clinical outcomes in patients with cardiac implantable electronic devices.

BACKGROUND: Kidney function may promote progression of AF. OBJECTIVE: We evaluated the association of kidney function to AF progression and resultant clinical outcomes in patients with cardiac implantable electronic devices (CIED). METHODS: We performed a retrospective cohort study using national clinical data from the Veterans Health Administration linked to CIED data from the Carelink® remote monitoring data warehouse (Medtronic Inc, Mounds View, MN). All devices had atrial leads and at least 75% of remote monitoring transmission coverage. Patients were included at the date of the first AF episode lasting ≥6 minutes, and followed until the occurrence of persistent AF in the first year, defined as ≥7 consecutive days with continuous AF. We used Cox regression analyses with persistent AF as a time-varying covariate to examine the association to stroke, myocardial infarction, heart failure and death. RESULTS: Of, 10,323 eligible patients, 1,771 had a first CIED-detected AF (mean age 69 ± 10 years, 1.2% female). In the first year 355 (20%) developed persistent AF. Kidney function was not associated with persistent AF after multivariable adjustment including CHA2DS2-VASc variables and prior medications. Only higher age increased the risk (HR: 1.37 per 10 years; 95% CI:1.22-1.54). Persistent AF was associated to higher risk of heart failure (HR: 2.27; 95% CI: 1.88-2.74) and death (HR: 1.60; 95% CI: 1.30-1.96), but not stroke (HR: 1.28; 95% CI: 0.62-2.62) or myocardial infarction (HR: 1.43; 95% CI: 0.91-2.25). CONCLUSION: Kidney function was not associated to AF progression, whereas higher age was. Preventing AF progression could reduce the risk of heart failure and death.

Telemedicine in the Coronavirus Disease 2019 Pandemic: A Pediatric Rehabilitation Perspective.

ABSTRACT: In the spring of 2020, coronavirus disease 2019 evolved into a worldwide pandemic, forcing traditional face-to-face healthcare to a standstill. Telemedicine was quickly adopted as a major tool for pediatric rehabilitation services. This article describes the national legislative response of the United States to the coronavirus disease 2019 pandemic and the opportunities and challenges of implementing telemedicine in pediatric rehabilitation outpatient settings, consultations, as well as physician and patient education. The feasibility of performing a remote pediatric musculoskeletal and neurological tele-evaluation is also discussed. Although challenges exist, telemedicine has demonstrated its potential and has proven to be a practical system. Future developments in technology and accessibility, in addition to support from government and third-party payers, have the potential to make telemedicine an effective and vital platform in a coordinated healthcare system.

Remote FEV1 Monitoring in Asthma Patients: A Pilot Study.

Forced expiratory volume in one second (FEV1 ) is a critical parameter for the assessment of lung function for both clinical care and research in patients with asthma. While asthma is defined by variable airflow obstruction, FEV1 is typically assessed during clinic visits. Mobile spirometry (mSpirometry) allows more frequent measurements of FEV1 , resulting in a more continuous assessment of lung function over time and its variability. Twelve patients with moderate asthma were recruited in a single-center study and were instructed to perform pulmonary function tests at home twice daily for 28 days and weekly in the clinic. Daily and mean subject compliances were summarized. The agreement between clinic and mobile FEV1 was assessed using correlation and Bland-Altman analyses. The test-retest reliability for clinic and mSpirometry was assessed by interclass correlation coefficient (ICC). Simulation was conducted to explore if mSpirometry could improve statistical power over clinic counterparts. The mean subject compliance with mSpirometry was 70% for twice-daily and 85% for at least once-daily. The mSpirometry FEV1 were highly correlated and agreed with clinic ones from the same morning (r = 0.993) and the same afternoon (r = 0.988) with smaller mean difference for the afternoon (0.0019 L) than morning (0.0126 L) measurements. The test-retest reliability of mobile (ICC = 0.932) and clinic (ICC = 0.942) spirometry were comparable. Our simulation analysis indicated greater power using dense mSpirometry than sparse clinic measurements. Overall, we have demonstrated good compliance for repeated at-home mSpirometry, high agreement and comparable test-retest reliability with clinic counterparts, greater statistical power, suggesting a potential for use in asthma clinical research.

Remote Fingerstick Blood Collection for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Testing.

The rapid worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has propelled the rapid development of serologic tests that can detect anti-SARS-CoV-2 antibodies. These have been used for studying the prevalence and spread of infection in different populations, and helping establish a recent diagnosis of coronavirus disease 2019 (COVID-19), and will likely be used to confirm humoral immunity after infection or vaccination. However, nearly all lab-based high-throughput SARS-CoV-2 serologic assays require a serum sample from venous blood draw, limiting their applications and scalability. Here, we present a method that enables large-scale SARS-CoV-2 serologic studies by combining self or office collection of fingerprick blood with a volumetric absorptive microsampling device (Mitra, Neoteryx LLC) with a high-throughput electrochemiluminescence-based SARS-CoV-2 total antibody assay (Roche Elecsys, Roche Diagnostics Inc) that is emergency use authorization approved for use on serum samples and widely used by clinical laboratories around the world. We found that the Roche Elecsys assay has a high dynamic range that allows for accurate detection of SARS-CoV-2 antibodies in serum samples diluted 1:20 as well as contrived dried blood extracts. Extracts of dried blood from Mitra devices acquired in a community seroprevalence study showed near identical sensitivity and specificity in detection of SARS-CoV-2 antibodies compared with neat sera using predefined thresholds for each specimen type. Overall, this study affirms the use of Mitra dried blood collection device with the Roche Elecsys SARS-CoV-2 total antibody assay for remote or at-home testing as well as large-scale community seroprevalence studies.

Marine animal tracking with classical and emerging localization algorithms.

Localization algorithms applied to acoustic tags for tracking marine animals can also be used to localize marine robots.

Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources.

Knowing the displacement capacity and mobility patterns of industrially exploited (i.e., fished) marine resources is key to establishing effective conservation management strategies in human-impacted marine ecosystems. Acquiring accurate behavioral information of deep-sea fished ecosystems is necessary to establish the sizes of marine protected areas within the framework of large international societal programs (e.g., European Community H2020, as part of the Blue Growth economic strategy). However, such information is currently scarce, and high-frequency and prolonged data collection is rarely available. Here, we report the implementation of autonomous underwater vehicles and remotely operated vehicles as an aid for acoustic long-baseline localization systems for autonomous tracking of Norway lobster (Nephrops norvegicus), one of the key living resources exploited in European waters. In combination with seafloor moored acoustic receivers, we detected and tracked the movements of 33 tagged lobsters at 400-m depth for more than 3 months. We also identified the best procedures to localize both the acoustic receivers and the tagged lobsters, based on algorithms designed for off-the-shelf acoustic tags identification. Autonomous mobile platforms that deliver data on animal behavior beyond traditional fixed platform capabilities represent an advance for prolonged, in situ monitoring of deep-sea benthic animal behavior at meter spatial scales.

Ultra-broadband local active noise control with remote acoustic sensing.

One enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specific location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person's ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state-of-the-art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this "virtual ANC headphone" system, a lightweight retro-reflective membrane pick-up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real-time with minimal invasiveness. The membrane design and the effects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound fields are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range (from 500 Hz to 6 kHz) under a complex sound field and for several common types of synthesised environmental noise, even in the presence of head motion.

Multidrone aerial surveys of penguin colonies in Antarctica.

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adélie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.

Counting penguins with drones.

A multidrone path-planning algorithm enables drones to scout Adélie penguin colonies in Antarctica.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Bio-optical evidence for increasing Phaeocystis dominance in the Barents Sea.

Increasing contributions of prymnesiophytes such as Phaeocystis pouchetii and Emiliania huxleyi to Barents Sea (BS) phytoplankton production have been suggested based on in situ observations of phytoplankton community composition, but the scattered and discontinuous nature of these records confounds simple inference of community change or its relationship to salient environmental variables. However, provided that meaningful assessments of phytoplankton community composition can be inferred based on their optical characteristics, ocean-colour records offer a potential means to develop a synthesis between sporadic in situ observations. Existing remote-sensing algorithms to retrieve phytoplankton functional types based on chlorophyll-a (chl-a) concentration or indices of pigment packaging may, however, fail to distinguish Phaeocystis from other blooms of phytoplankton with high pigment packaging, such as diatoms. We develop a novel algorithm to distinguish major phytoplankton functional types in the BS and apply it to the MODIS-Aqua ocean-colour record, to study changes in the composition of BS phytoplankton blooms in July, between 2002 and 2018, creating time series of the spatial distribution and intensity of coccolithophore, diatom and Phaeocystis blooms. We confirm a north-eastward expansion in coccolithophore bloom distribution, identified in previous studies, and suggest an inferred increase in chl-a concentrations, reported by previous researchers, may be partly explained by increasing frequencies of Phaeocystis blooms. This article is part of the theme issue 'The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.

Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt.

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.

Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors.

For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.

Comparative migration ecology of striped bass and Atlantic sturgeon in the US Southern mid-Atlantic bight flyway.

Seasonal migrations are key to the production and persistence of marine fish populations but movements within shelf migration corridors or, "flyways", are poorly known. Atlantic sturgeon and striped bass, two critical anadromous species, are known for their extensive migrations along the US Mid-Atlantic Bight. Seasonal patterns of habitat selection have been described within spawning rivers, estuaries,and shelf foraging habitats, but information on the location and timing of key coastal migrations is limited. Using a gradient-based array of acoustic telemetry receivers, we compared the seasonal incidence and movement behavior of these species in the near-shelf region of Maryland, USA. Atlantic sturgeon incidence was highest in the spring and fall and tended to be biased toward shallow regions, while striped bass had increased presence during spring and winter months and selected deeper waters. Incidence was transient (mean = ~2 d) for both species with a pattern of increased residency (>2 d) during autumn and winter, particularly for striped bass, with many individuals exhibiting prolonged presence on the outer shelf during winter. Flyways also differed spatially between northern and southern migrations for both species and were related to temperature: striped bass were more likely to occur in cool conditions while Atlantic sturgeon preferred warmer temperatures. Observed timing and spatial distribution within the Mid-Atlantic flyway were dynamic between years and sensitive to climate variables. As shelf ecosystems come under increasing maritime development, gridded telemetry designs represent a feasible approach to provide impact responses within key marine flyways like those that occur within the US Mid-Atlantic Bight.

An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments.

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10-5° and 2.01 × 10-5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient's locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5 µm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10 km vs. 3 km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.