Maximum likelihood separation of anthropogenic and wind-generated underwater noise.

A method is presented for simultaneous estimation of the probability distributions of both anthropogenic and wind-generated underwater noise power spectral density using only acoustic data recorded with a single hydrophone. Probability density models for both noise sources are suggested, and the model parameters are estimated using the method of maximum likelihood. A generic mixture model is utilized to model a time invariant anthropogenic noise distribution. Wind-generated noise is assumed normally distributed with a wind speed-dependent mean. The mean is then modeled as an affine linear function of the wind-generated noise level at a reference frequency, selected in a frequency range where the anthropogenic noise is less dominant. The method was used to successfully estimate the wind-generated noise spectra from ambient noise recordings collected at two locations in the southern Baltic Sea. At the North location, 3 km from the nearest shipping lane, the ship noise surpasses the wind-generated noise almost 100% of the time in the frequency band 63-400 Hz during summer for wind speed 7 m/s. At the South location, 14 km to the nearest shipping lane, the ship noise dominance is lower but still 40%-90% in the same frequencies and wind speed.

Modeling, Detecting, and Tracking Freezing of Gait in Parkinson Disease Using Inertial Sensors.

In this paper, we develop new methods to automatically detect the onset and duration of freezing of gait (FOG) in people with Parkinson disease (PD) in real time, using inertial sensors. We first build a physical model that describes the trembling motion during the FOG events. Then, we design a generalized likelihood ratio test framework to develop a two-stage detector for determining the zero-velocity and trembling events during gait. Thereafter, to filter out falsely detected FOG events, we develop a point-process filter that combines the output of the detectors with information about the speed of the foot, provided by a foot-mounted inertial navigation system. We computed the probability of FOG by using the point-process filter to determine the onset and duration of the FOG event. Finally, we validate the performance of the proposed system design using real data obtained from people with PD who performed a set of gait tasks. We compare our FOG detection results with an existing method that only uses accelerometer data. The results indicate that our method yields 81.03% accuracy in detecting FOG events and a threefold decrease in the false-alarm rate relative to the existing method.

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.

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.