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As digital health technology becomes more pervasive, machine learning (ML) provides a robust way to analyze and interpret the myriad of collected features. The purpose of this preliminary work was to use ML classification to assess the benefits and relevance of neurocognitive features both tablet-based assessments and self-reported metrics, as they relate to Parkinson's Disease (PD) and its stages [Hoehn and Yahr (H&Y) Stages 1–5]. Further, this work aims to compare perceived versus sensor-based neurocognitive abilities. In this study, 75 participants ( n = 50 PD; n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multifunction), functional movement assessments (e.g., Berg Balance Scale), and standardized health questionnaires (e.g., PDQ-39). Decision tree classification of sensor-based features allowed for the discrimination of PD from healthy controls with an accuracy of 92.6 % , and early and advanced stages of PD with an accuracy of 73.7 % ; compared to the current gold standard tools [e.g., standardized health questionnaires ( 78.3 % accuracy) and functional movement assessments ( 70 % accuracy)]. Significant features were also identified using decision tree classification. Device magnitude of acceleration was significant in 12 of 14 tests ( 85.7 % ), regardless of test type. For classification between diagnosed and control populations, 17 motor (e.g., device magnitude of acceleration), 9 accuracy (e.g., number of correct/incorrect interactions), and 8 timing features (e.g., time to between interactions) were significant. For classification between early (H&Y Stages 1 and 2) and advanced (H&Y Stages 3, 4, and 5) stages of PD, 7 motor, 12 accuracy, and 14 timing features were significant. Finally, this work depicts that perceived functionality of individuals with PD differed from sensor-based functionalities. In early-stage PD was shown to be 21.6 % lower than sensor-based scores with notable perceived deficits in memory and executive function. However, individuals in advanced stages had elevated perceptions (1.57x) for executive and behavioral functions compared to early-stage populations. Machine learning in digital health systems allows for a more comprehensive understanding of neurodegenerative diseases and their stages and may also depict new features that influence the ways digital health technology should be configured. [ABSTRACT FROM AUTHOR]

This work is based on the observation that existing energy management techniques for mobile devices, such as Dynamic Voltage Scaling (DVS), are non-cooperative in the sense that they reduce the energy consumption of a single device, disregarding potential consequences for other constraints (e.g., end-to-end deadlines) and/or other devices (e.g., energy consumption on neighboring devices). This paper argues that energy management in distributed wireless real-time systems has to be end-to-end in nature, requiring a coordinated approach among communicating devices. A cooperative distributed energy management technique (Co-DVS) is proposed that (1) adapts and maintains end-to-end latencies within specified timeliness requirements (deadlines) and (2) enhances energy savings at the devices with the highest pay-off factors that represent the relative benefits or significance of conserving energy at a device. The proposed technique employs a feedback-based approach to dynamically distribute end-to-end slack among the devices based on their pay-off factors. [ABSTRACT FROM AUTHOR]

Low-overhead resource monitoring is key to the successful management of distributed high-performance computing environments, particularly when applications have well-defined quality of service (QoS) requirements. The dproc system-level monitoring mechanisms provide tools both for efficiently monitoring system-level events and for notifying remote hosts of events relevant to their operation. Implemented as extension to the Linux kernel, dproc provides several key functions. First, utilizing the familiar /proc virtual filesystem, dproc extends this interface with resource information collected from both local and remote hosts. Second, to predictably capture and distribute monitoring information, dproc uses a kernel-level group communication facility, termed KECho, which implements events and event channels. Third, and the focus of this paper, is dproc's run-time customizability for resource monitoring, which includes the generation and deployment of monitoring functionality within remote operating system kernels. Using dproc, we show that (a) data streams can be customized according to a client's resource availabilities (dynamic stream management), (b) by dynamically varying distributed monitoring (dynamic filtering of monitoring information), an appropriate balance can be maintained between monitoring overheads and application quality, and (c) by performing monitoring at kernel-level, the information captured enables decision making that takes into account the multiple resources used by applications. [ABSTRACT FROM AUTHOR]