Inertial sensor array processing with motion models

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordBy arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this paper, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

IMU-based smartphone-to-vehicle positioning

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn this paper, we address the problem of using inertial measurements to position a smartphone with respect to a vehicle-fixed accelerometer. Using rigid body kinematics, this is cast as a nonlinear filtering problem. Unlike previous publications, we consider the complete three-dimensional kinematics, and do not approximate the angular acceleration to be zero. The accuracy of an estimator based on the unscented Kalman filter is compared with the Cramer-Rao bound. As is illustrated, the estimates can be expected to be better in the horizontal plane than in the vertical direction of the vehicle frame. Moreover, implementation issues are discussed and the system model is motivated by observability arguments. The efficiency of the method is demonstrated in a field study which shows that the horizontal RMSE is in the order of 0.5 [m]. Last, the proposed estimator is benchmarked against the state-of-the-art in left/right classification. The framework can be expected to find use in both insurance telematics and distracted driving solutions

Fusion of OBD and GNSS Measurements of Speed

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.There are two primary sources of sensor measurements for driver behavior profiling within insurance telematics and fleet management. The first is the on-board diagnostics system, typically found within most modern cars. The second is the global navigation satellite system, whose associated receivers commonly are embedded into smartphones or off-the-shelf telematics devices. In this paper, we present maximum likelihood and maximum a posteriori estimators for the problem of fusing speed measurements from these two sources to jointly estimate a vehicle's speed and the scale factor of the wheel speed sensors. In addition, we analyze the performance of the estimators by use of the Cramér-Rao bound, and discuss the estimation of model parameters describing measurement errors and vehicle dynamics. Last, simulations and real-world data are used to show that the proposed estimators yield a substantial performance gain compared to when employing only one of the two measurement sources

Alternative EM algorithms for nonlinear state-space models

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThe expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton’s method. We propose alternative expectationmaximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization

Map-aided dead-reckoning using only measurements of speed

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordWe present a particle-based framework for estimating the position of a vehicle using map information and measurements of speed. The filter propagates the particles’ position estimates by means of dead-reckoning, and then updates the particle weights using two measurement functions. The first measurement function is based on the assumption that the lateral force on the vehicle does not exceed critical limits derived from physical constraints. The second is based on the assumption that the driver approaches a target speed derived from the speed limits along the upcoming trajectory. Assuming some prior knowledge of the initial position, performance evaluations of the proposed method indicate that end destinations often can be estimated with an accuracy in the order of 100 [m]. These results expose the sensitivity and commercial value of speed data collected in many of today’s insurance telematics programs, where the data is used to adjust premiums and provide driver feedback. We end by discussing the strengths and weaknesses of different methods for anonymization and privacy preservation in telematics programs

The β-model—maximum likelihood, Cramér–Rao bounds, and hypothesis testing

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordWe study the maximum-likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level. The model generalizes the well-known β-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the undirected β-model, the directed β-model, and the covariate-based β-model. The corresponding maximum-likelihood estimators are compared with the bounds by means of simulations. Moreover, examples are given on how to use the presented maximum-likelihood estimators to test for directionality and significance. Finally, the applicability of the model is demonstrated using temporal social network data describing communication among healthcare workers

Internalization of a polysialic acid-binding Escherichia coli bacteriophage into eukaryotic neuroblastoma cells

Eukaryotic organisms are continuously exposed to bacteriophages, which are efficient gene transfer agents in bacteria. However, bacteriophages are considered not to pass the eukaryotic cell membrane and enter nonphagocytic cells. Here we report the binding and penetration of Escherichia coli PK1A2 bacteriophage into live eukaryotic neuroblastoma cells in vitro. The phage interacts with cell surface polysialic acid, which shares structural similarity with the bacterial phage receptor. Using fluorescence and electron microscopy, we show that phages are internalized via the endolysosomal route and persist inside the human cells up to one day without affecting cell viability. Phage capsid integrity is lost in lysosomes, and the phage DNA is eventually degraded. We did not detect the entry of phage DNA into the nucleus; however, we speculate that this might occur as a rare event, and propose that this potential mechanism could explain prokaryote-eukaryote gene flow.Peer reviewe

Smartphone placement within vehicles

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordSmartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label “Held by hand” are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations

A Hidden Markov Model for Seismocardiography

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.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 9 [ms], 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