Joaquín Recas

Development and Evaluation of Multilead Wavelet-Based ECG Delineation Algorithms for Embedded Wireless Sensor Nodes

This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG) wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems.

Evaluation and design exploration of solar harvested-energy prediction algorithm

To respond to variations in solar energy, harvested-energy prediction is essential to harvested-energy management approaches. The effectiveness of such approaches is dependent on both the achievable accuracy and computation overhead of prediction algorithm implementation. This paper presents detailed evaluation of a recently reported solar energy prediction algorithm to determine empirical bounds on achievable accuracy and implementation overhead using an effective error evaluation technique. We evaluate the algorithm performance over varying prediction horizons and propose guidelines for algorithm parameter selection across different real solar energy profiles to simplify implementation. The prediction algorithm computation overhead is measured on actual hardware to demonstrate prediction accuracy-cost trade-off. Finally, we motivate the basis for dynamic prediction algorithm and show that more than 10% increase in prediction accuracy can be achieved compared to static algorithm.

Accurate Human Tissue Characterization for Energy-Efficient Wireless On-Body Communications

The demand for Wireless Body Sensor Networks (WBSNs) is rapidly increasing due to the revolution in wearable systems demonstrated by the penetration of on-the-body sensors in hospitals, sports medicine and general health-care practices. In WBSN, the body acts as a communication channel for the propagation of electromagnetic (EM) waves, where losses are mainly due to absorption of power in the tissue. This paper shows the effects of the dielectric properties of biological tissues in the signal strength and, for the first time, relates these effects with the human body composition. After a careful analysis of results, this work proposes a reactive algorithm for power transmission to alleviate the effect of body movement and body type. This policy achieves up to 40.8% energy savings in a realistic scenario with no performance overhead.

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 simulation 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.

Generic Markov Model of the Contention Access Period of IEEE 802.15.4 MAC Layer

Abstract The IEEE-802.15.4 standard is poised to become the global standard for low data rate, low energy consumption Wireless Sensor Networks. By assigning the same sets of contention access parameters for all data frames and nodes, the Contention Access Period (CAP) of the slotted IEEE-802.15.4 currently provides an even channel access functionality and no service differentiation. However, some applications may require service differentiation and traffic prioritization support to accommodate high-priority traffic (e.g., alarms). In order to simulate a scenario in which different sets of access parameters for different node classes can be configured, this paper develops a Markov-chain-based model of the CAP of the IEEE-802.15.4-MAC. Our Markov model can be used to evaluate the impact of mixing node classes in important factors like the throughput, energy consumption, probability of delivery and the packet latency. The model has been used to provide traffic differentiation in a high saturation scenario in which a set of nodes can be configured to increase 76% the probability of sending a packet and reduce 58% latency, with a 69% energy penalty, in comparison with a standard scenario. The accuracy of the Markov model is validated by extensive ns-2 simulations.

Proactive and reactive transmission power control for energy-efficient on-body communications.

In wireless body sensor network (WBSNs), the human body has an important effect on the performance of the communication due to the temporal variations caused and the attenuation and fluctuation of the path loss. This fact suggests that the transmission power must adapt to the current state of the link in a way that it ensures a balance between energy consumption and packet loss. In this paper, we validate our two transmission power level policies (reactive and predictive approaches) using the Castalia simulator. The integration of our experimental measurements in the simulator allows us to easily evaluate complex scenarios, avoiding the difficulties associated with a practical realization. Our results show that both schemes perform satisfactorily, providing overall energy savings of 24% and 22% for a case of study, as compared to the maximum transmission power mode.

Channel analysis and dynamic adaptation for energy-efficient WBSNs

Wireless Sensor Networks (WSNs) have recently emerged as a premier research topic, while the increasing use of this technology has empowered the development of Wireless Body Sensor Networks (WBSNs). In this environment, the power consumption of the sensor nodes has to be optimized in order to extend the battery duration but, at the same time, several factors like the body position and movement impact on the quality of the communication and the power transmission. In this paper, we analyze the effect of body positioning, body movement and body type in the wireless communication and the power requirements. The obtained results for real subjects performing common tasks has allowed the envisioning of a reactive mechanism to alleviate the negative effect of these factors and minimize the power consumption of the radio.

A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform real-time delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in runtime to a wide range of modes and sampling rates, from a ultralow-power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete high-accuracy delineation mode, in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in the case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography Committee in the high-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultralow-power 8-MHz TI MSP430 series microcontroller ranges from 0.2% to 8.5% according to the mode used.