https://github.com/deepcharles/gait-data
Data sets of gait time series
https://github.com/deepcharles/gait-data
Last synced: about 1 year ago
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Data sets of gait time series
- Host: GitHub
- URL: https://github.com/deepcharles/gait-data
- Owner: deepcharles
- Created: 2019-05-02T19:35:33.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-25T16:07:56.000Z (about 6 years ago)
- Last Synced: 2025-04-13T21:11:45.777Z (about 1 year ago)
- Language: Python
- Size: 7.81 KB
- Stars: 9
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Data sets for the study of human locomotion with inertial measurements units
The data provided in this repository are described in the following article:
- Truong, C., Barrois-Müller, R., Moreau, T., Provost, C., Vienne-Jumeau, A., Moreau, A., Vidal, P.-P., Vayatis, N., Buffat, S., Yelnik, A., Ricard, D., & Oudre, L. (2019). A data set for the study of human locomotion with inertial measurements units. Image Processing On Line (IPOL), 9. [[abstract]](https://deepcharles.github.io/publication/ipol-data-2019) [[doi]](https://doi.org/10.5201/ipol.2019.265) [[pdf]](http://deepcharles.github.io/files/ipol-walk-data-2019.pdf) [[online demo]](http://ipolcore.ipol.im/demo/clientApp/demo.html?id=265)
Please cite this article whenever you want to make a reference to this data set.
A simple online exploration tool is available [online](http://ipolcore.ipol.im/demo/clientApp/demo.html?id=77777000084).
Data can be downloaded as a zipped archive (GaitData.zip, ~200MB):
- [link 1](https://mycore.core-cloud.net/index.php/s/sTk4Vq8N3zefvKH/download)
- [link 2](http://dev.ipol.im/~truong/GaitData.zip)
Once extracted, the data can be read using the following code snippets (in Python, R). Be sure to execute those lines while in the same directory as the extracted `GaitData` folder.
#### Python
Signals are loaded into [Pandas](https://pandas.pydata.org/) data frames. Please be sure to have it installed (`pip install pandas`).
```python
from load_data import get_code_list, load_trial, load_metadata
# Load and manipulate all signals and metadata.
all_codes = get_code_list()
print("There are {} trials.".format(len(all_codes)))
for code in all_codes:
signal = load_trial(code) # pandas data frame
metadata = load_metadata(code) # dictionary
# Do something.
# ...
```
#### R
Be sure to set the working directory (with the function `setwd`) to wherever the data file has been unzipped. To read JSON files, the package `jsonlite` must be installed.
```R
library("jsonlite")
code_list <- fromJSON("code_list.json")
for(code in code_list){
signal <- read.csv(paste(code, ".csv", sep=""))
metadata <- fromJSON(paste(code, ".json", sep=""))
# Do something.
# ...
}
```