OS-Based Sensor Node Platform and Energy Estimation Model for Health-Care Wireless Sensor Networks

Accurate power and performance figures are critical to assess the effective design of possible sensor node architectures in Body Area Networks (BANs) since they operate on limited energy storage. Therefore, accurate power models and simulation tools that can model real-life working conditions need to be developed and validated with real platforms. In this paper we propose a sensor node platform designed for health-care applications and a validated simulation model based on event-driven operating system simula- tion that can be used to accurately analyze performance and power consumption in BANs composed of multiple nodes. Thus, this model can be employed to tune the node architecture and communication layer for different working conditions, applications and topologies of BANs. In this paper we validate the proposed simulation model on different real life applications and working conditions. Our results show variations of less than 4% between the presented simulation framework and measurements in the final platforms

La reducción de los costes de transporte en España (1800-1936)

This paper describes the improvement that took place in the Spanish transport system between 1800 and 1936. The text points out that, despite the investment efforts that were carried out between 1840 and 1855, the process of transport cost reduction only experienced substantial progress after 1855. The largest transport cost decrease of the period under consideration took place during the three decades between 1855 and the great depression of the late nineteenth century, through the substitution of the railroad for the traditional transport means in the main routes of the country, as well as through the gradual reduction of the price of railway transport. The process went on more slowly later on, thanks to the construction of additional raillway lines (until 1895) and the enlargement of the secondary road network. The process of transport cost reduction accelerated again from the 1920s onwards, thanks to the diffusion of the automobile technology

Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering

Abstract-Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person's body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithmbased clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons between individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate

Bifocal disruption of the knee extensor apparatus (floating patella) in a 72-year-old patient

We present the unusual case of a simultaneous ipsilateral rupture of the quadriceps tendon and the patellar ligament in a 72-year-old male patient. No predisposing factors were diagnosed. After surgical treatment, the patient healed with full function and full range of motion. © 2011, Acta Orthopaedica Belgica.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Wearable physiological sensors reflect mental stress state in office-like situations

Timely mental stress detection can help to prevent stress-related health problems. The aim of this study was to identify those physiological signals and features suitable for detecting mental stress in office-like situations. Electrocardiogram (ECG), respiration, skin conductance and surface electromyogram (sEMG) of the upper trapezius muscle were measured with a wearable system during three distinctive stress tests. The protocol contained stress tests that were designed to represent office-like situations. Generalized Estimating Equations were used to classify the data into rest and stress conditions. We reached an average classification rate of 74.5%. This approach may be used for continuous stress measurement in daily office life to detect mental stress at an early stage

Low-power wearable sensing for preventive healthcare

Low-power wearable sensing will soon allow the quantitative and continuous measurement of health parameters. In this paper we illustrate how wearable sensors can be used to track activity and energy expenditure, and measure stress. Soon such information may empower people in managing their own health, and provide the necessary data to enable a preventive approach to healthcare

Towards ambulatory mental stress measurement from physiological parameters

Ambulatory mental stress monitoring requires longterm physiological measurements. This paper presents a data collection protocol for ambulatory recording of physiological parameters for stress measurement purposes. We present a wearable sensor system for ambulatory recording of ECG, EMG, respiration and skin conductance. The system also records various context parameters: acceleration, temperature and relative humidity. We show that the sensor system is capable of long-term, noninvasive, nonobtrusive, wireless physiological monitoring. We also show some preliminary results of a stress estimation method. These results reveal already a number of context-related issues we will have to take into account in future work. The presented sensor system enables physiological and context data collection and further development of personalized real-time stress detection algorithms

Low-power robust beat detection in ambulatory cardiac monitoring

\u3cp\u3eWith new advances in ambulatory monitoring new challenges appear due to degradation in signal quality and limitations in hardware requirements. Existing signal analysis methods should be re-evaluated in order to adapt to the restrictive requirements of these new applications. With this motivation, we chose a robust beat detection algorithm and optimized it further to be running in an embedded platform within a cardiac monitoring sensor node. The algorithm was designed in floating point in Matlab and evaluated in order to study its performance under a wide range of conditions. The initial PC version of the algorithm obtained a good performance under a wide variety of conditions (Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database and Se=99.88%, +P=99.93% on our own database with ambulatory data). In this study, the algorithm is adapted and further optimized to work in real time on an embedded digital processor, while keeping this performance without degradation. The run-time memory usage of the application was of 150 KB with an execution time of 1.5 million cycles and an average power consumption of 494 μW for an ECG of 3 seconds length and sampling frequency of 198 Hz. The algorithm implementation in a general purpose processor will put significant limits on the performance in terms of power consumption. We propose possible specifications for an application-optimized processor for more efficient ECG analysis.\u3c/p\u3

Towards Mental Stress Detection Using Wearable Physiological Sensors

Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor