Cardiorespiratory fitness estimation using wearable sensors: laboratory and free-living analysis of context-specific submaximal heart rates

In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R2) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R2 for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R2 varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance.

Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation.In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations.We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM).Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines.NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Influences of physical oceanographic processes on chlorophyll distributions in coastal and estuarine waters of the South Atlantic Bight

Coastal and estuarine waters of the South Atlantic Bight are highly productive, with primary production of 600-700 gC/m2/y. While controls and fate of this production are conceptually well understood, the importance of meteorology and physical circulation processes on phytoplankton has not received equivalent attention. Here, we describe the effects of wind stress and tidal currents on temporal and spatial distributions of phytoplankton biomass represented as chlorophyll a (chl a). Moored instruments were deployed and shipboard sampling was conducted in the North Edisto estuary (South Carolina) and adjacent inner shelf waters during four, two-week field studies in May and August 1993, and June and September 1994. Local wind regimes induced upwelling- and downwelling-favorable conditions which strengthened or reduced vertical density stratification in the coastal frontal zone, respectively, and shifted the location of the front. Chl a in shelf waters was more or less homogenous independent of the wind regime, while chl a on the estuary delta was generally vertically stratified. Within the estuary, chl a concentrations were positively correlated with the alongshore component of wind stress; chl a was not correlated with the weaker cross-shelf component of wind stress. Highest chl a occurred during strong downwelling-favorable events. The quick response time to wind forcing (6-12 hrs) implied a direct effect on chl a distributions and not a stimulation of growth processes. The source of the elevated chl a in response to wind forcing was apparently resuspension of settled and epibenthic algal cells. Tidal currents also influenced the vertical distribution and concentration of chl a. Time series sampling on the estuary delta showed that, with increasing velocity of ebb and flood tide currents, the relative contributions of pennate and centric diatoms with attached detritus and sand grains also increased, indicating that tidal resuspension of settled and epibenthic microalgae also occurred. Vertical stratification of chl a (highest concentrations near the bottom) began to degrade upon mixing by tidal currents with velocities as low as 10 cm/sec. Homogenization of 5-7 m water columns was fully achieved at velocities of 20-30 cm/sec. The data document the direct and comparatively immediate (timescales of minuteshours) impact of tidal and wind energy on concentrations and distribution patterns of phytoplankton in coastal and estuarine waters of the South Atlantic Bight

Microcities: A Platform Based on Microclouds for Neighborhood Services

International audienceThe current datacenter-centralized architecture limits the cloud to the location of the datacenters, generally far from the user. This architecture collides with the latest trend of ubiquity of Cloud computing. Distance leads to increased utilization of the broadband Wide Area Network and poor user experience, especially for interactive applications. A semi-decentralized approach can provide a better Quality of Experience (QoE) in large urban populations in mobile cloud networks, by confining local traffic near the user while maintaining centralized characteristics, running on the users and network devices. In this paper, we propose a novel semi-decentralized cloud architecture based on microclouds. Microclouds are dynamically created and allow users to contribute resources from their computers, mobile and network devices to the cloud. Microclouds provide a dynamic and scalable system without an extra investment in infrastructure. We also provide a description of a realistic mobile cloud use case, and its adaptation to microclouds

Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines

<p>Abstract</p> <p>Background</p> <p>The literature suggests a beneficial effect of motor imagery (MI) if combined with physical practice, but detailed descriptions of MI training session (MITS) elements and temporal parameters are lacking. The aim of this review was to identify the characteristics of a successful MITS and compare these for different disciplines, MI session types, task focus, age, gender and MI modification during intervention.</p> <p>Methods</p> <p>An extended systematic literature search using 24 databases was performed for five disciplines: Education, Medicine, Music, Psychology and Sports. References that described an MI intervention that focused on motor skills, performance or strength improvement were included. Information describing 17 MITS elements was extracted based on the PETTLEP (physical, environment, timing, task, learning, emotion, perspective) approach. Seven elements describing the MITS temporal parameters were calculated: study duration, intervention duration, MITS duration, total MITS count, MITS per week, MI trials per MITS and total MI training time.</p> <p>Results</p> <p>Both independent reviewers found 96% congruity, which was tested on a random sample of 20% of all references. After selection, 133 studies reporting 141 MI interventions were included. The locations of the MITS and position of the participants during MI were task-specific. Participants received acoustic detailed MI instructions, which were mostly standardised and live. During MI practice, participants kept their eyes closed. MI training was performed from an internal perspective with a kinaesthetic mode. Changes in MI content, duration and dosage were reported in 31 MI interventions. Familiarisation sessions before the start of the MI intervention were mentioned in 17 reports. MI interventions focused with decreasing relevance on motor-, cognitive- and strength-focused tasks. Average study intervention lasted 34 days, with participants practicing MI on average three times per week for 17 minutes, with 34 MI trials. Average total MI time was 178 minutes including 13 MITS. Reporting rate varied between 25.5% and 95.5%.</p> <p>Conclusions</p> <p>MITS elements of successful interventions were individual, supervised and non-directed sessions, added after physical practice. Successful design characteristics were dominant in the Psychology literature, in interventions focusing on motor and strength-related tasks, in interventions with participants aged 20 to 29 years old, and in MI interventions including participants of both genders. Systematic searching of the MI literature was constrained by the lack of a defined MeSH term.</p

An acceptance model for the adoption of smart glasses technology by healthcare professionals

In the recent years, there has been an increase in the interest from different industries in the adoption of smart wearable devices in the light of their inevitable ubiquity. One type of these devices is the Augmented Reality Smart Glasses (ARSGs), which can have great effect in different areas through providing timely information to users. One of the industries that can significantly reap the benefits of this technology is healthcare. However, as healthcare is a very multi-dimensional industry, there is a need for a multifaceted look into the adoption and acceptance of smart glasses by health professionals. This study tends to examine the acceptance of smart glasses by healthcare professionals based on Technology Acceptance Model (TAM) as there is an imperative for empirical studies on user perceptions, attitudes, and intentions. For this purpose, five external factors are extracted from the literature and field study, being integration with information systems, external effects, hands-free feature, technological compatibility, and documentation. The model is examined by using PLS-SEM methodology. This study found documentation to have the strongest impact on intention due to the substitution of paperwork by mobile devices and facilitation of continuous documentation