Download Data Set (7755.7 MB)
Download Healthy_Raw h5 Files, Part 1 (10761.3 MB)
Download Healthy_Raw h5 Files, Part 2 (10421.4 MB)
Download Healthy_Raw h5 Files, Part 3 (9560.5 MB)
Download Healthy_Raw h5 Files, Part 4 (9275.3 MB)
Download Stroke_Raw OMX Files (1797.2 MB)
Download Stroke_Raw h5 Files (12202.7 MB)
Download Codes_Figures (5.6 MB)
The first file (which can be downloaded via the "Download Data Set" button) contains the OMX data files for healthy adults (Healthy_Raw_OMX_Files.zip).
The additional files include:
- 4 ZIP folders of h5 data files for healthy adults
- 1 ZIP folder of OMX data files for adults who've had strokes
- 1 ZIP folder of h5 data files for adults who've had strokes
- 1 ZIP folder of additional PY, IPYNB, and PDF files (Codes_DataProcess.zip)
Wearable technologies may offer ways to measure unhindered activities of daily living (ADL) among patients who had stroke in their natural settings. This study investigated new visualization driven timeseries extraction methods for classification of activities from stroke and healthy adults. Stroke and healthy adults wore a sensor at L5/S1 area for three consecutive days and data was collected passively in participant’s naturalistic environment. Data visualization resulted in selected timeseries which were classified with an accuracy of 97.3% using recurrent neural network. We found negative correlation between body mass index BMI and higher acceleration fraction. We also found individuals with stroke produced activity amplitudes lower than healthy counterparts in all activity bands (low, medium and high). Our findings demonstrate that intelligently extracted visualization-driven timeseries and a recurrent neural network can accurately classify movements amongst stroke and healthy groups. This use of visualization based timeseries extraction from naturalistic data is a major strength of this study and lays foundation for passive ADL monitoring in real-world environments. Such timeseries extraction methods along with RNN classifiers have potential to track the daily progress of rehabilitation exercises remotely among stroke survivors.
Wearable sensor, Stroke rehabilitation, Activities of Daily Living (ADL), Visualization Techniques
Other Rehabilitation and Therapy | Physical Therapy
Soangra R, John J. Differentiating stroke and healthy adults using visualization driven timeseries extraction method [data set]. 2022. https://doi.org/10.36837/chapman.000334
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This work is licensed under a Creative Commons Attribution 4.0 License.