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Duty cycling (DC) is a popular technique for energy conservation in wireless sensor networks that allows nodes to wake up and sleep periodically. Typically, a single packet transmission (SPT) occurs per cycle, leading to possibly long delay. With aggregated packet transmission (APT), nodes transmit a batch of packets in a single cycle. The potential benefits brought by an APT scheme include shorter delay, higher throughput and higher energy efficiency. In the literature, different analytical models have been proposed to evaluate the performance of SPT schemes. However, no analytical models for the APT mode on synchronous DC medium access control mechanisms exist. In this paper, we first develop a three-dimensional (3D) discrete time Markov chain (DTMC) model to evaluate the performance of an APT scheme with packet retransmission enabled. The proposed model captures the dynamics of the state of the queue of nodes and the retransmission status, as well as the evolution of the number of active nodes in the network, i.e., nodes with a non-empty queue. We then study the number of retransmissions needed to transmit a packet successfully. Based on the observations, we develop another less complex DTMC model with infinite retransmissions which embodies only two dimensions. Furthermore, we extend the 3D model into a four-dimensional model by considering error-prone channel conditions. The proposed models are adopted to determine packet delay, throughput, packet loss, energy consumption, and energy efficiency. Furthermore the analytical models are validated through discrete-event based simulations. Numerical results show that an APT scheme achieves substantially better performance than its SPT counterpart in terms of delay, throughput, packet loss and energy efficiency, and that the developed analytical models reveal precisely the behavior of the APT scheme.

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Ballistocardiography is a non-invasive measurement of the mechanical movement of the body caused by cardiac ejection of blood. Recent studies have demonstrated that ballistocardiogram (BCG) signals can be measured using a modified home weighing scale, and used to track changes in myocardial contractility and cardiac output. With this approach, the BCG can potentially be used both for preventive screening and for chronic disease management applications. However, for achieving high signal quality, subjects are required to stand still on the scale in an upright position for the measurement; the effects of intentional (for user comfort) or unintentional (due to user error) modifications in the position or posture of the subject during the measurement have not been investigated in the existing literature. In this study, we quantified the effects of different standing and seated postures on the measured BCG signals, and on the most salient BCG-derived features compared to reference standard measurements (e.g., impedance cardiography). We determined that the standing upright posture led to the least distorted signals as hypothesized, and that the correlation between BCG-derived timing interval features (R-J interval) and the pre-ejection period, PEP (measured using ICG), decreased significantly with impaired posture or sitting position. We further implemented two novel approaches to improve the PEP estimates from other standing and sitting postures, using system identification and improved J-wave detection methods. These approaches can improve the usability of standing BCG measurements in unsupervised settings (i.e. the home), by improving the robustness to non-ideal posture, as well as enabling high quality seated BCG measurements.

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We introduce the Spectrum-averaged Harmonic Path (SHAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. Periodic movement of human torso caused by respiration and heart beat induces fundamental frequencies and their harmonics at the respiration and heart rates. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods of identifying the fundamental component either in frequency or time domain to estimate the HR and/or RR lead to significant error if the fundamental is distorted or cancelled by interference. The SHAPA algorithm (1) takes advantage of the HR harmonics, where there is less interference, and (2) exploits the information in previous spectra to achieve more reliable and robust estimation of the fundamental frequency in the spectrum under consideration. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference and produces 16% to 60% more valid estimates.