Medicine and Health Sciences Commons^™

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 …