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1. Investigation of Methods to Extract Fetal Electrocardiogram from the Mother’s Abdominal Signal in Practical Scenarios

   https://doi.org/10.3390/technologies8020033  

   Sarafan, Sadaf ; Le, Tai ; Naderi, Amir Mohammad ; Nguyen, Quoc-Dinh ; Kuo, Brandon Tiang-Yu ; Ghirmai, Tadesse ; Han, Huy-Dung ; Lau, Michael P. ; Cao, Hung ( June 2020 , Technologies)

   null (Ed.)

   Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health. 

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2. Angular Velocity Estimation using Non-coplanar Accelerometer Array

   https://doi.org/10.1109/JSEN.2021.3107470  

   Maynard, Michael ; Vikas, Vishesh ( August 2021 , IEEE Sensors Journal)

   null (Ed.)

   Over the last few decades, Gyro-Free Inertial Measurement Units (GF-IMUs) have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar spacial arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an Extended Kalman Filter (EKF) that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube.They analyze the estimation error for static and dynamic movement as the distance between the accelerometers (four accelerometers in cube-orientation) is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard error of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using Vicon® to construct the ground truth angular velocity. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises. 

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3. A Visual Inertial Odometry Framework for 3D Points, Lines and Planes

   https://doi.org/10.1109/IROS51168.2021.9636526  

   Kannapiran, Shenbagaraj ; van Baar, Jeroen ; Berman, Spring ( September 2021 , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in feature-poor environments, i.e. low-texture and/or low-light environments. In this paper, we aim to address the challenge of recovering rigid registration between successive camera poses in feature-poor environments in a Visual Inertial Odometry (VIO) setting. In addition to inertial sensing, we instrument a small aerial robot with an RGBD camera and propose a framework that unifies the incorporation of 3D geometric entities: points, lines, and planes. The tracked 3D geometric entities provide constraints in an Extended Kalman Filtering framework. We show that by directly exploiting 3D geometric entities, we can achieve improved registration. We demonstrate our approach on different texture-poor environments, with some containing only flat texture-less surfaces providing essentially no 2D features for tracking. In addition, we evaluate how the addition of different 3D geometric entities contributes to improved pose estimation by comparing an estimated pose trajectory to a ground truth pose trajectory obtained from a motion capture system. We consider computationally efficient methods for detecting 3D points, lines and planes, since our goal is to implement our approach on small mobile robots, such as drones. 

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4. Contact-aided invariant extended Kalman filtering for robot state estimation

   https://doi.org/10.1177/0278364919894385  

   Hartley, Ross ; Ghaffari, Maani ; Eustice, Ryan M ; Grizzle, Jessy W ( March 2020 , The International Journal of Robotics Research)

   Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system’s trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based extended Kalman filter (EKF) through both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system. 

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5. Fetal Electrocardiogram Extraction from the Mother’s Abdominal Signal Using the Ensemble Kalman Filter

   https://doi.org/10.3390/s22072788  

   Sarafan, Sadaf ; Le, Tai ; Lau, Michael P. ; Hameed, Afshan ; Ghirmai, Tadesse ; Cao, Hung ( April 2022 , Sensors)

   Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm. 

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