No abstract

To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject-and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients' daily life.

This paper explores the cloud- versus server-based deployment scenarios of an enhanced computer vision platform for potential deployment on low-resolution 511 traffic video streams. An existing computer vision algorithm based on a spatial–temporal map and designed for high-angle traffic video like that of NGSIM (Next Generation SIMulation) is enhanced for roadside CCTV traffic camera angles. Because of the lower visual angle, determining the directions, splitting vehicles from occlusions, and identifying lane changes become difficult. A motion-flow-based direction determination method, a bisection occlusion detection and splitting algorithm, and a lane-change tracking method are proposed. The model evaluation is conducted by using videos from multiple cameras from the New Jersey Department of Transportation’s 511 traffic video surveillance system. The results show promising performance in both accuracy and computational efficiency for potential large-scale cloud deployment. The cost analysis reveals that at the current pricing model of cloud computing, the cloud-based deployment is more convenient and cost-effective for an on-demand network assessment. In contrast, the dedicated-server-based deployment is more economical for long-term traffic detection deployment.

Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.

No abstract