Search

Background: Mobile devices and their capabilities (e.g., device sensors and human-device interactions) are increasingly being considered for use in clinical assessments and disease monitoring due to their ability to provide objective, repeatable, and more accurate measures of neurocognitive performance. These mobile-based assessments also provide a foundation for the design of intervention recommendations. Objective: The purpose of this work was to assess the benefits of various physical intervention programs as they relate to Parkinson's disease (PD), its symptoms, and stages (Hoehn and Yahr (H&Y) Stages 1–5). Methods: Ninety-five participants (n = 70 PD; n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multi-function) and standardized health questionnaires. 208 symptom-specific digital features were normalized to assess the benefits of various physical intervention programs (e.g., aerobic activity, non-contact boxing, functional strength, and yoga) for individuals with PD. While previous studies have shown that physical interventions improve both motor and non-motor PD symptoms, this paper expands on previous works by mapping symptom-specific neurocognitive functionalities to specific physical intervention programs across stages of PD. Results: For early-stage PD (e.g., H&Y Stages 1 & 2), functional strength activities provided the largest overall significant delta improvement (Δ= 0.1883; p = 0.0265), whereas aerobic activity provided the largest overall significant delta improvement (Δ= 0.2700; p = 0.0364) for advanced stages of PD (e.g., H&Y Stages 3–5). Conclusions: As mobile-based digital health technology allows for the collection of larger, labeled, objective datasets, new ways to analyze and interpret patterns in this data emerge which can ultimately lead to new personalized medicine programs. [ABSTRACT FROM AUTHOR]

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]

Background: Due to the COVID-19 pandemic, beneficial physical intervention classes for individuals with Parkinson's disease (PD) were cancelled. Objective: To understand effects of the COVID-19 stay-at-home mandate and the inability to participate in recommended and structured physical interventions as a consequence of these mandates, specifically designed mobile assessments were used that collected both self-reporting information and objective task-based metrics of neurocognitive functions to assess symptom changes for individuals with PD. Methods: Self-reporting questionnaires focusing on overall quality of life (e.g., when individuals typically feel at their best, changes in activity levels, and symptom progression) were given to all individuals (n = 28). In addition, mobile-based neurocognitive assessments were administered to a subset of the population (n = 8) to quantitatively assess changes due to COVID-19 restrictions. Results: The highest self-reported factors in which individuals denoted feeling their best were after exercise (67.86%) and being in a comfortable and supportive environment (60.71%). Objective measures found overall duration of physical activity during the stay-at-home mandate decreased significantly (p = 0.022). With the lack of overall activity, 82.14%of individuals self-reported having at least one symptom that worsened moderately or higher. Further testing, using mobile-based assessments, showed average completion times of functional tasks increased, taking about 2.1 times longer, while accuracy metrics showed overall degradation. Conclusion: Although the COVID-19 stay-at-home mandate was intended to help protect individuals at high risk from coming into contact with the virus, it also prevented individuals from receiving recommended supervised exercise interventions resulting in significant negative effects in social well-being and across motor and speech neurocognitive tasks for individuals with PD. [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]

Abstract: Wireless multicast for multihop ad-hoc networks ensures that the same information is delivered efficiently to multiple recipients simultaneously. However, existing protocols ignore the real-time nature of many wireless applications such as real-time video and audio communications in mesh and ad-hoc networks. Further, devices in such networks are typically energy-constrained, i.e., multicast protocols have to trade off timeliness with energy efficiency. This paper presents RTMC, a real-time multicast protocol for wireless multihop networks that allows receivers to specify their individual real-time constraints and establishes multicast trees such that receivers’ deadlines are met and overall energy consumption in the network is reduced. Toward this end, RTMC relies on dynamically prioritized packet scheduling on forwarding nodes, prioritized channel access at the MAC layer, feedback control for latency management, tree adaptations to address mobility, dynamic subscriptions and unsubscriptions, and adaptations to address link breakages. Simulation results show that, compared to non-real-time multicast techniques, RTMC can provide user-specific latency management while reducing the energy consumption in the network. [Copyright &y& Elsevier]