Reliable peak detection and feature extraction for wireless electrocardiograms.

The electrocardiogram (ECG) is a vital device to examine the electrical activities of the heart. It is useful for diagnosing cardiovascular diseases, which often manifest themselves through alterations in the ECG signals' characteristics. These alterations are primarily observed in the signals' key components: the Q, R, S, T, and P peaks.

A Comprehensive Multifunctional Approach for Measuring Parkinson's Disease Severity.

Objectives:  This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multifunctional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach.

Methods:  We recruited 37 individuals diagnosed with PD, each of whom completed a series of tablet-based neurocognitive tests assessing motor, memory, speech, executive functions, and tasks ranging in complexity from single to multifunctional.

Predicting long-term sleep deprivation using wearable sensors and health surveys.

Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognition, and mood. The measurement of sleep from in-bed physiological signals has progressed to where commercial devices already incorporate this functionality.

Mental Health Monitoring: Design Concept of a Smartphone Application.

Mental health (MH) has become a global issue. Digital phenotyping in mental healthcare provides a highly effective, scaled, cost-effective approach to handling global MH problems. We propose an MH monitoring application.

Classification of Parkinson's disease and its stages using machine learning.

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.

Towards Symptom-Specific Intervention Recommendation Systems.

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.

Negative Effects of COVID-19 Stay-at-Home Mandates on Physical Intervention Outcomes: A Preliminary Study.

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.

Enhancement of Neurocognitive Assessments Using Smartphone Capabilities: Systematic Review.

Background: Comprehensive exams such as the Dean-Woodcock Neuropsychological Assessment System, the Global Deterioration Scale, and the Boston Diagnostic Aphasia Examination are the gold standard for doctors and clinicians in the preliminary assessment and monitoring of neurocognitive function in conditions such as neurodegenerative diseases and acquired brain injuries (ABIs). In recent years, there has been an increased focus on implementing these exams on mobile devices to benefit from their configurable built-in sensors, in addition to scoring, interpretation, and storage capabilities. As smartphones become more accepted in health care among both users and clinicians, the ability to use device information (eg, device position, screen interactions, and app usage) for subject monitoring also increases.

The power of dynamic social networks to predict individuals' mental health.

Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to predict one's likelihood of being depressed or anxious from rich dynamic social network data. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without making any predictions; or they study other individual traits but not mental health.

Network analysis of the NetHealth data: exploring co-evolution of individuals' social network positions and physical activities.

Understanding the relationship between individuals' social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy ones. In this context, we explore the co-evolution of individuals' social network positions and physical activities. We are able to do so because the NetHealth study at the University of Notre Dame has generated both high-resolution longitudinal social network (e.

Deviations of acoustic low-level descriptors in speech features of a set of triplets, one with autism.

Verbal speech of children diagnosed with ASD is explored in order to identify patterns autism has left in speech, and to model such patterns for implementing automatic diagnostic and screening frameworks. In this study, we identify the deviations of acoustic low-level descriptors (LLDs) in voice of an autistic adolescent from her typically developing triplet siblings. The goal is to identify the atypicality in voice introduced by autism under minimum gender, age, genetic, and language bias and use the gained insights to build a more generalized model by adding more subjects hierarchically.

Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

Background: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab.

Design Factors of Longitudinal Smartphone-based Health Surveys.

Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness.

Portable mTBI Assessment Using Temporal and Frequency Analysis of Speech.

This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions.