Medicine and Health Sciences Commons^™

Quantifying Intra- And Interlimb Use During Unimanual And Bimanual Tasks In Persons With Hemiparesis Post-Stroke, Susan V. Duff, Aaron Miller, Lori Quinn, Gregory Youdan Jr., Lauri Bishop, Heather Ruthrauff, Eric Wade

Physical Therapy Faculty Articles and Research

Background

Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited.

Objective

To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance.

Methods

Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into …

Visualization-Driven Time-Series Extraction From Wearable Systems Can Facilitate Differentiation Of Passive Adl Characteristics Among Stroke And Healthy Older Adults, Joby John, Rahul Soangra

Physical Therapy Faculty Articles and Research

Wearable technologies allow the measurement of unhindered activities of daily living (ADL) among patients who had a stroke in their natural settings. However, methods to extract meaningful information from large multi-day datasets are limited. This study investigated new visualization-driven time-series extraction methods for distinguishing activities from stroke and healthy adults. Fourteen stroke and fourteen healthy adults wore a wearable sensor at the L5/S1 position for three consecutive days and collected accelerometer data passively in the participant’s naturalistic environment. Data from visualization facilitated selecting information-rich time series, which resulted in classification accuracy of 97.3% using recurrent neural networks (RNNs). Individuals with …

Kinematic Analysis Of 360° Turning In Stroke Survivors Using Wearable Motion Sensors, Masoud Abdollahi, Pranav Madhav Kuber, Michael Shiraishi, Rahul Soangra, Ehsan Rashedi

Physical Therapy Faculty Articles and Research

Background: A stroke often bequeaths surviving patients with impaired neuromusculoskeletal systems subjecting them to increased risk of injury (e.g., due to falls) even during activities of daily living. The risk of injuries to such individuals can be related to alterations in their movement. Using inertial sensors to record the digital biomarkers during turning could reveal the relevant turning alterations. Objectives: In this study, movement alterations in stroke survivors (SS) were studied and compared to healthy individuals (HI) in the entire turning task due to its requirement of synergistic application of multiple bodily systems. Methods: The motion of 28 participants (14 …