Medicine and Health Sciences Commons^™

Mobile Phone Sensors Can Discern Medication-Related Gait Quality Changes In Parkinson's Patients In The Home Environment, Albert Pierce, Niklas König Ignasiak, Wilford K. Eiteman-Pang, Cyril Rakovski, Vincent Berardi

Psychology Faculty Articles and Research

Patients with Parkinson's Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication …

An End-To-End Cnn With Attentional Mechanism Applied To Raw Eeg In A Bci Classification Task, Elnaz Lashgari, Jordan Ott, Akima Connelly, Pierre Baldi, Uri Maoz

Psychology Faculty Articles and Research

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based motor-imagery classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training …

Effects Of Goal Type And Reinforcement Type On Self-Reported Domain-Specific Walking Among Inactive Adults: 2×2 Factorial Randomized Controlled Trial, Mindy L. Mcentee, Alison Cantley, Emily Foreman, Vincent Berardi, Christine B. Phillips, Jane C. Hurley, Melbourne F. Hovell, Steven Hooker, Marc A. Adams

Psychology Faculty Articles and Research

Background: WalkIT Arizona was a 2×2 factorial trial examining the effects of goal type (adaptive versus static) and reinforcement type (immediate versus delayed) to increase moderate to vigorous physical activity (MVPA) among insufficiently active adults. The 12-month intervention combined mobile health (mHealth) technology with behavioral strategies to test scalable population-health approaches to increasing MVPA. Self-reported physical activity provided domain-specific information to help contextualize the intervention effects.

Objective: The aim of this study was to report on the secondary outcomes of self-reported walking for transportation and leisure over the course of the 12-month WalkIT intervention.

Methods: A total of …

Virtual Reality As A Training Tool To Treat Physical Inactivity In Children, Adam W. Kiefer, David Pincus, Michael J. Richardson, Gregory D. Myer

Psychology Faculty Articles and Research

Lack of adequate physical activity in children is an epidemic that can result in obesity and other poor health outcomes across the lifespan. Physical activity interventions focused on motor skill competence continue to be developed, but some interventions, such as neuromuscular training (NMT), may be limited in how early they can be implemented due to dependence on the child’s level of cognitive and perceptual-motor development. Early implementation of motor-rich activities that support motor skill development in children is critical for the development of healthy levels of physical activity that carry through into adulthood. Virtual reality (VR) training may be beneficial …