Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress.

The Autonomic Nervous System (ANS) plays a critical role in regulating cardiac functions. Early detection of ANS dysfunctions is crucial for preventing or slowing the progression of cardiovascular diseases. Current methods for analyzing ANS activity, such as heart rate variability analysis and muscle sympathetic nerve activity recording, face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit personalized health assessments. This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments. The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males) and diabetic subjects (8 females and 6 males). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p<0.05), and also suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p<0.05). The MMARCS model represents an innovative computational approach for quantifying ANS functionality, offering potential benefits for clinical measurements of cardiovascular, disease progression monitoring, and home health monitoring via wearable technology. Its balance between physiological accuracy and model simplicity makes it a promising tool for personalized health assessments.

Quantitative Assessment of Autonomic Regulation of the Cardiac System.

Autonomic neural system (ANS) regulates the circulation to provide optimal perfusion of every organ in accordance with its metabolic needs, and the quantitative assessment of autonomic regulation is crucial for personalized medicine in cardiovascular diseases. In this paper, we propose the Dystatis to quantitatively evaluate autonomic regulation of the human cardiac system, based on homeostatis and probabilistic graphic model, where homeostatis explains ANS regulation while the probability graphic model systematically defines the regulation process for quantitative assessment. The indices and measurement methods for three well-designed scenarios are also illustrated to evaluate the proposed Dystatis: (1) heart rate variability (HRV), blood pressure variability (BPV), and respiration synchronization (Synch) in resting situation; (2) chronotropic competence indices (CCI) in graded exercise testing; and (3) baroreflex sensitivity (BRS), sympathetic nerve activity (SNA), and parasympathetic nerve activity (PNA) in orthostatic testing. The previous clinical results have shown that the proposed method and indices for autonomic cardiac system regulation have great potential in prediction, diagnosis, and rehabilitation of cardiovascular diseases, hypertension, and diabetes.

A personalized-model-based central aortic pressure estimation method.

Central Aortic Pressure (CAP) can be used to predict cardiovascular structural damage and cardiovascular events, and the development of simple, well-validated and non-invasive methods for CAP waveforms estimation is critical to facilitate the routine clinical applications of CAP. Existing widely applied methods, such as generalized transfer function (GTF-CAP) method and N-Point Moving Average (NPMA-CAP) method, are based on clinical practices, and lack a mathematical foundation. Those methods also have inherent drawback that there is no personalisation, and missing individual aortic characteristics. To overcome this pitfall, we present a personalized-model-based central aortic pressure estimation method (PM-CAP)in this paper. This PM-CAP has a mathematical foundation: a human aortic network model is proposed which is developed based on viscous fluid mechanics theory and could be personalized conveniently. Via measuring the pulse wave at the proximal and distal ends of the radial artery, the least square method is then proposed to estimate patient-specific circuit parameters. Thus the central aortic pulse wave can be obtained via calculating the transfer function between the radial artery and central aorta. An invasive validation study with 18 subjects comparing PM-CAP with direct aortic root pressure measurements during percutaneous transluminal coronary intervention was carried out at the Beijing Hospital. The experimental results show better performance of the PM-CAP method compared to the GTF-CAP method and NPMA-CAP method, which illustrates the feasibility and effectiveness of the proposed method.

Self-contained pedestrian tracking during normal walking using an inertial/magnetic sensor module.

This paper proposes a novel self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity updates, but also applies the stride information to further correct the acceleration double integration drifts and thus improves the tracking accuracy. In our method, a velocity control variable is designed in the process model, which is set to the average velocity derived from stride information in the swing (nonzero velocity) phases or zero in the stance (zero-velocity) phases. Stride-based position information is also derived as the pseudomeasurements to further improve the accuracy of the position estimates. An adaptive Kalman filter is then designed to fuse all the sensor information and pseudomeasurements. The proposed pedestrian tracking method has been extensively evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors, and have been shown to perform effectively for pedestrian tracking.

Hierarchical information fusion for global displacement estimation in microsensor motion capture.

This paper presents a novel hierarchical information fusion algorithm to obtain human global displacement for different gait patterns, including walking, running, and hopping based on seven body-worn inertial and magnetic measurement units. In the first-level sensor fusion, the orientation for each segment is achieved by a complementary Kalman filter (CKF) which compensates for the orientation error of the inertial navigation system solution through its error state vector. For each foot segment, the displacement is also estimated by the CKF, and zero velocity update is included for the drift reduction in foot displacement estimation. Based on the segment orientations and left/right foot locations, two global displacement estimates can be acquired from left/right lower limb separately using a linked biomechanical model. In the second-level geometric fusion, another Kalman filter is deployed to compensate for the difference between the two estimates from the sensor fusion and get more accurate overall global displacement estimation. The updated global displacement will be transmitted to left/right foot based on the human lower biomechanical model to restrict the drifts in both feet displacements. The experimental results have shown that our proposed method can accurately estimate human locomotion for the three different gait patterns with regard to the optical motion tracker.

Ubiquitous human upper-limb motion estimation using wearable sensors.

Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

A dynamic conditional random field model for foreground and shadow segmentation.

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.

Spatiotemporal video segmentation based on graphical models.

This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.