Difference between revisions of "Gait events"
Siddhartha (Talk | contribs) |
Siddhartha (Talk | contribs) |
||
| Line 20: | Line 20: | ||
'''Input data''': AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.] | '''Input data''': AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.] | ||
| − | '''Input data format''': abc.mat | + | '''Input data format''': abc.mat [Data in Matlab format.] |
'''''NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.''''' | '''''NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.''''' | ||
Revision as of 15:23, 20 October 2016
Gait Event Detection in Real-World Environments for Long-Term Applications
This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running.
The library can be found here: Link to Github repository
List of Specifications
Activities: Walking and running
Placement of 3-axis Accelerometer: Anywhere around the ankle in any orientation.
Sensitivity of the Accelerometer: (+-) 4g or more. Please specify. Please also check if the accelerometer signal has saturated during intense activity such as running.
Sampling Frequency: Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable. Please specify.]
Input data: AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.]
Input data format: abc.mat [Data in Matlab format.]
NOTE: The data should consist of ONLY walking and running segments of the signal. Segments corresponding to inactivity or any other activity should be removed from the signals prior to data submission.
[1] Siddhartha Khandelwal; Nicholas Wickström, "Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis," in IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.PP, no.99, pp.1-1
[2] Siddhartha Khandelwal; Nicholas Wickström, "Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis", in BIOSIGNALS 2014, 7th International Conference on Bio-inspired Systems and Signal Processing, Angers, France, March 3-6, 2014
DiVA url: [3]
