No abstract

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

No abstract

The last two decades have witnessed how threedimensional (3D) point cloud scanning and registration algorithms have become increasingly popular in areas as diverse as cinematography, robotics, and medicine, among others. Despite their broad application range, such algorithms remain to be computationally demanding and difficult to implement. In particular, the task of choosing and implementing suitable registration-pipeline processes for specific applications continues to be challenging in most practical cases. This paper presents the implementation of a point cloud stitching system to produce 360° 3D images from individual, partial views of a solid model. Performance analyses and evaluations supporting the decisionmaking process allow for identifying factors leading to the best accuracy and computational speed of the iterative closest point (ICP) registration algorithms considered for the task at hand. The outcomes of our analysis lead to interesting findings related to two well-known ICP variants, while also providing useful implementation guidelines for developing a practical 360° 3D scanning system.

No abstract