NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

Sensors

Luis Gustavo Nonato¹  Rodrigo Colnago Contreras¹  Heidi Schambra²  Avinash Parnandi²  Bruno Gomes Coelho³  Claudio Silva³ 

[1] Department of Applied Mathematics and Statistics, Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, SP, Brazil;School of Medicine, New York University, New York, NY 10017, USA;Tandon School of Engineering, New York University, New York, NY 10012, USA;

关键词: visualization;    visual analytics;    graph learning;    stroke;    set theory;   

DOI  :  10.3390/s21134482

来源: DOAJ

【 摘 要 】

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

【 授权许可】

Unknown