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Wahlström, J., Skog, I., Händel, P., Khosrow-Khavar, F., Tavakolian, K., Stein, P. K. & Nehorai, A. (2017). A Hidden Markov Model for Seismocardiography. IEEE Transactions on Biomedical Engineering, 64(10), 2361-2372
Open this publication in new window or tab >>A Hidden Markov Model for Seismocardiography
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2017 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 64, no 10, p. 2361-2372Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Cardiac time intervals, heart rate variability (HRV), hidden Markov model (HMM), seismocardiogram (SCG)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-215801 (URN)10.1109/TBME.2017.2648741 (DOI)000411585100006 ()28092512 (PubMedID)2-s2.0-85026447100 (Scopus ID)
Note

QC 20171018

Available from: 2017-10-18 Created: 2017-10-18 Last updated: 2017-10-18Bibliographically approved
Skog, I., Karagiannis, I., Bergsten, A. B., Harden, J., Gustafsson, L. & Handel, P. (2017). A Smart Sensor Node for the Internet-of-Elevators-Non-Invasive Condition and Fault Monitoring. IEEE Sensors Journal, 17(16), 5198-5208
Open this publication in new window or tab >>A Smart Sensor Node for the Internet-of-Elevators-Non-Invasive Condition and Fault Monitoring
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2017 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 17, no 16, p. 5198-5208Article in journal (Refereed) Published
Abstract [en]

The signal processing scheme of a smart sensor node for the Internet-of-Elevators is presented. The sensor node is a self-contained black box unit only requiring power to be supplied, which enables a cost efficient way to modernize existing elevator systems in terms of condition monitoring capabilities. The sensor node monitors the position of the elevator using an inertial navigation system in conjugation with a simultaneous localization and mapping framework. Features reflecting the elevator system's operation and health condition are calculated by evaluating the ride quality parameters defined by the ISO 18738-1 standards, the vibration versus frequency spectrum, and the vibration versus position spectrum. Abnormal stops are identified by detecting decelerations that deviate from the typical deceleration pattern of the elevator or when the stopping position of the elevator does not match the learned floor levels. Furthermore, the condition of the door system is monitored by tracking the magnetic field variations that the motion of the doors creates; the number of door openings and the time required for the doors to close are estimated. The capability and performance of the blacksignal processing scheme are illustrated through a series of experiments. The experiments show, inter alia, that using low-cost sensors similar to those in a smartphone, the position of the elevator car can, with 99.9% probability, be estimated with an error of less than 1 m for travels up to 43 s long. The experiments also indicate that small degradations in the doors' closing time can be detected from the magnetic field measurements.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-211732 (URN)10.1109/JSEN.2017.2719630 (DOI)000406310500022 ()2-s2.0-85021837627 (Scopus ID)
Note

QC 20170816

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-12-05Bibliographically approved
Larsson, R., Skog, I. & Händel, P. (2017). Inertial Sensor Driven Smartphone and Automobile Coordinate System Alignment. In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN. IEEE
Open this publication in new window or tab >>Inertial Sensor Driven Smartphone and Automobile Coordinate System Alignment
2017 (English)In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

In this study a method is presented for estimating the orientation of an inertial measurement unit (IMU) located within an automobile, using only the measurements from the IMU itself. The orientation estimation problem is posed as a non-linear filtering probletn, which is solved using a marginalized particle filter. The performance of the proposed method is evaluated using a large collection of real-world data, collected by multiple drivers. The drivers used their own smartphones and had no restrictions on smartphone handling during drives. The orientation accuracy achieved with the proposed method is in the order of a few degrees; 50% of cases were below 5 degrees and 90% of cases were below 20 degrees.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Keywords
IMU alipunent, Smartphone sensors, Smart-phone telematics
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-230877 (URN)10.1109/ITSC.2017.8317592 (DOI)000432373000011 ()2-s2.0-85046288064 (Scopus ID)978-1-5386-1526-3 (ISBN)
Conference
20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN
Note

QC 20180618

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Carlsson, H., Skog, I. & Jaldén, J. (2017). On-The-Fly Geometric Calibration of Inertial Sensor Arrays. In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN): . Paper presented at 8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN.
Open this publication in new window or tab >>On-The-Fly Geometric Calibration of Inertial Sensor Arrays
2017 (English)In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2017Conference paper, Published paper (Refereed)
Abstract [en]

We present a maximum likelihood estimator for estimating the positions of accelerometers in an inertial sensor array. This method simultaneously estimates the positions of the accelerometers and the motion dynamics of the inertial sensor array and, therefore, does not require a predefined motion sequence nor any external equipment. Using an iterative block coordinate descent optimization strategy, the calibration problem can be solved with a complexity that is linear in the number of time samples. The proposed method is evaluated by Monte-Carlo simulations of an inertial sensor array built out of 32 inertial measurement units. The simulation results show that, if the array experiences sufficient dynamics, the position error is inversely proportional to the number of time samples used in the calibration sequence. Further, results show that for the considered array geometry and motion dynamics in the order of 2000 degrees/s and 2000 degrees/s(2), the positions of the accelerometers can be estimated with an accuracy in the order of 10(-6) m using only 1000 time samples. This enables fast on-the-fly calibration of the geometric errors in an inertial sensor array by simply twisting it by hand for a few seconds.

Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-220651 (URN)000417415600018 ()978-1-5090-6299-7 (ISBN)
Conference
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-02-20Bibliographically approved
Wahlström, J., Skog, I. & Händel, P. (2017). Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary. IEEE transactions on intelligent transportation systems (Print), 18(10), 2802-2825
Open this publication in new window or tab >>Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 10, p. 2802-2825Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-216625 (URN)10.1109/TITS.2017.2680468 (DOI)000412223300020 ()
Note

QC 20171102

Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2018-03-07Bibliographically approved
Wahlström, J., Skog, I., La Rosa, P. S., Händel, P. & Nehorai, A. (2017). The beta-Model-Maximum Likelihood, Cramer-Rao Bounds, and Hypothesis Testing. IEEE Transactions on Signal Processing, 65(12), 3234-3246
Open this publication in new window or tab >>The beta-Model-Maximum Likelihood, Cramer-Rao Bounds, and Hypothesis Testing
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2017 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 12, p. 3234-3246Article in journal (Refereed) Published
Abstract [en]

We 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 beta-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the undirected beta-model, the directed beta-model, and the covariate-based beta-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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
The beta-model, Cramer-Rao bounds, hypothesis testing, random graphs, dynamic social networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-207647 (URN)10.1109/TSP.2017.2691667 (DOI)000399947200015 ()2-s2.0-85019089340 (Scopus ID)
Note

QC 20170607

Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-07Bibliographically approved
Nilsson, J.-O. & Skog, I. (2016). Inertial Sensor Arrays - A Literature Review. In: 2016 EUROPEAN NAVIGATION CONFERENCE (ENC): . Paper presented at European Navigation Conference (ENC), MAY 30-JUN 02, 2016, HELSINKI, FINLAND. IEEE
Open this publication in new window or tab >>Inertial Sensor Arrays - A Literature Review
2016 (English)In: 2016 EUROPEAN NAVIGATION CONFERENCE (ENC), IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Inertial sensor arrays present the possibility of improved and extended sensing capabilities as compared to customary inertial sensor setups. Inertial sensor arrays have been studied since the 1960s and have recently received a renewed interest, mainly thanks to the ubiquitous micro-electromechanical (MEMS) inertial sensors. However, the number of variants and features of inertial sensor arrays and their disparate applications makes the literature spread out. Therefore, in this paper we provide a brief summary and literature review on the topic of inertial sensor arrays. Publications are categorized and presented in a structured way; references to +300 publications are provide. Finally, an outlook on the main research challenges and opportunities related to inertial sensor arrays is given.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-200779 (URN)10.1109/EURONAV.2016.7530551 (DOI)000391255800014 ()2-s2.0-84992107932 (Scopus ID)978-1-4799-8915-7 (ISBN)
Conference
European Navigation Conference (ENC), MAY 30-JUN 02, 2016, HELSINKI, FINLAND
Note

QC 20170206

Available from: 2017-02-06 Created: 2017-02-02 Last updated: 2017-02-06Bibliographically approved
Skog, I., Nilsson, J.-O., Händel, P. & Nehorai, A. (2015). Arrays of single-chip IMUs. In: Proc. International Conference on Indoor Positioning and Indoor Navigation (IPIN): . Paper presented at 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015),October 13-16 2015, Alberta, Canada. Calgary, Canada: University of Calgary
Open this publication in new window or tab >>Arrays of single-chip IMUs
2015 (English)In: Proc. International Conference on Indoor Positioning and Indoor Navigation (IPIN), Calgary, Canada: University of Calgary , 2015Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The development of ultralow-cost single-chip IMUs nowmake it feasible to construct massive IMU arrays. Such arrays giveproperties not attainable by single IMUs. Specifically, non-colocatedaccelerometers provide rotational information with complementary char-acteristics to that provided by the gyroscopes. In this poster we reviewthe signal model of multi-IMU systems and present experimental resultsfrom an in-house developed IMU array.

Place, publisher, year, edition, pages
Calgary, Canada: University of Calgary, 2015
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-183083 (URN)
Conference
2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015),October 13-16 2015, Alberta, Canada
Note

QC 20160617

Available from: 2016-02-27 Created: 2016-02-27 Last updated: 2016-06-17Bibliographically approved
Wahlström, J., Skog, I. & Händel, P. (2015). Detection of Dangerous Cornering in GNSS-Data-Driven Insurance Telematics. IEEE transactions on intelligent transportation systems (Print), 16(6)
Open this publication in new window or tab >>Detection of Dangerous Cornering in GNSS-Data-Driven Insurance Telematics
2015 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 16, no 6Article in journal (Refereed) Published
Abstract [en]

We propose a framework for the detection of dangerous vehicle cornering events, based on statistics related to the no-sliding and no-rollover conditions. The input variables are estimated using an unscented Kalman filter applied to global navigation satellite system (GNSS) measurements of position, speed, and bearing. The resulting test statistic is evaluated in a field study where three smartphones are used as measurement probes. A general framework for performance evaluation and estimator calibration is presented as depending on a generic loss function. Furthermore, we introduce loss functions designed for applications aiming to either minimize the number of missed detections and false alarms, or to estimate the risk level in each cornering event. Finally, the performance characteristics of the estimator are presented as depending on the detection threshold, as well as on design parameters describing the driving behavior. Since the estimation only uses GNSS measurements, the framework is particularly well suited for smartphone-based insurance telematics applications, aiming to avoid the logistic and monetary costs associated with, e.g., on-board-diagnostics or black-box dependent solutions. The design of the estimation algorithm allows for instant feedback to be given to the driver and, hence, supports the inclusion of real-time value-added services in usage-based insurance programs.

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
UBI, insurance telematics, GNSS, vehicle lateral forces, unscented Kalman filtering
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-180244 (URN)10.1109/TITS.2015.2431293 (DOI)000365958500009 ()2-s2.0-84961056641 (Scopus ID)
Note

QC 20160108

Available from: 2016-01-08 Created: 2016-01-08 Last updated: 2017-12-01Bibliographically approved
Wahlström, J., Skog, I. & Händel, P. (2015). Driving behavior analysis for smartphone-based insurance telematics. In: WPA 2015 - Proceedings of the 2nd Workshop on Physical Analytics: . Paper presented at 2nd Workshop on Physical Analytics, WPA 2015, 22 May 2015 (pp. 19-24). ACM Digital Library
Open this publication in new window or tab >>Driving behavior analysis for smartphone-based insurance telematics
2015 (English)In: WPA 2015 - Proceedings of the 2nd Workshop on Physical Analytics, ACM Digital Library, 2015, p. 19-24Conference paper, Published paper (Refereed)
Abstract [en]

Insurance telematics programs are continuously gaining market shares in the automotive insurance industry. By recording data on drivers' behavior, the information asymmetry between the policyholder and the insurer is reduced, enabling a granular risk differentiation based on the true risk levels of the drivers. However, the growth of the insurance telematics industry is being held up by large logistic costs associated with the process of acquiring data. As a result, several market participants have started looking towards smartphone-based solutions, which have the potential of easing and improving the data collection process for both policyholders and insurers. In this paper, we present a unified framework highlighting the challenges of smartphone-based driver behavior analysis. Since all driver behavior analysis relies on access to accurate navigation data, we first address the intermediate step of smartphone-based automotive navigation. The considered topics include estimation of the smartphone's orientation with respect to the vehicle, classification of the smartphoneowner as a passenger or driver, and navigation in GNSSchallenged areas. Once a driver-specific high-performance navigation solution has been obtained, it can be used to extract information on the driver's behavior. We review the most commonly employed driving events, and discuss some of the difficulties inherent in detecting these events.

Place, publisher, year, edition, pages
ACM Digital Library, 2015
Keywords
Driver behavior analysis, Insurance telematics, Smartphones, Behavioral research, Commerce, Competition, Insurance, Navigation, Signal encoding, Wireless telecommunication systems, Data collection process, Driver behavior, Extract informations, Information asymmetry, Market participants, Navigation solution, Risk differentiation, Telematics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-196189 (URN)10.1145/2753497.2753535 (DOI)2-s2.0-84982082560 (Scopus ID)9781450334983 (ISBN)
Conference
2nd Workshop on Physical Analytics, WPA 2015, 22 May 2015
Note

Conference Paper. QC 20161115

Available from: 2016-11-15 Created: 2016-11-14 Last updated: 2016-11-15Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3054-6413

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