A Hidden Markov Model for Seismocardiography.

We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.

Quantum 1/f effect in resonant biochemical piezoelectric and MEMS sensors.

Piezoelectric sensors used for the detection of chemical agents and as electronic nose instruments are based on bulk and surface acoustic wave resonators. Adsorption of gas molecules on the surface of the polymer coating is detected by a reduction of the resonance frequency of the quartz disk, subject also to fundamental quantum 1/f frequency fluctuations. The quantum 1/f limit of detection is given by the quantum 1/f formula for quartz resonators. Therefore, for quantum 1/f optimization and for calculation and improvement of the fundamental sensitivity limits, we must avoid closeness of the crystal size to the phonon coherence length, which corresponds to the maximum error and minimal sensitivity situation, as shown here. Adsorbed masses below the pg range can be detected. Microelectromechanical system (MEMS) resonators have provided a possibility for the nanominiaturization of these sensors. Essential for integrated nanotechnology, these resonant silicon bars (fingers) are excited magnetically or electrically through external applied forces, since they are not piezoelectric or magnetostrictive. The application of the quantum 1/f theory to these systems is published here for the first time. It provides simple formulas that yield much lower quantum 1/f frequency fluctuations for magnetic excitation, in comparison with electrostatically driven MEMS resonators.

Zero-velocity detection --- an algorithm evaluation.

In this study, we investigate the problem of detecting time epochs when zero-velocity updates can be applied in a foot-mounted inertial navigation (motion tracking) system. We examine three commonly used detectors: the acceleration moving variance detector, the acceleration magnitude detector, and the angular rate energy detector. We demonstrate that all detectors can be derived within the same general likelihood ratio test framework given the different prior knowledge about the sensor signals. Further, by combining all prior knowledge, we derive a new likelihood ratio test detector. Subsequently, we develop a methodology to evaluate the performance of the detectors. Employing the developed methodology, we evaluate the performance of the detectors using leveled ground, slow (approx. 3 km/h) and normal (approx. 5 km/h) gait data. The test results are presented in terms of detection versus false-alarm probability. Our preliminary results shows that the new detector performs marginally better than the angular rate energy detector that outperforms both the acceleration moving variance detector and the acceleration magnitude detector.