Gait events - Revision history

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2016-12-12T13:10:00Z

Siddhartha at 21:13, 24 October 2016

2016-10-24T21:13:04Z

Siddhartha at 16:35, 24 October 2016

2016-10-24T16:35:19Z

Siddhartha at 16:11, 24 October 2016

2016-10-24T16:11:53Z

Siddhartha at 15:18, 24 October 2016

2016-10-24T15:18:09Z

Siddhartha at 15:16, 24 October 2016

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Siddhartha at 14:23, 20 October 2016

2016-10-20T14:23:37Z

Siddhartha at 14:22, 20 October 2016

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← Older revision		Revision as of 09:52, 21 October 2018
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−		+	This page no longer exists
−	~~== Gait Event Detection in Real-World Environments for Long-Term Applications ==~~
−
−
−	Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.
−
−	[[File:accPlacement.jpg\|right\|thumb\|200px\|caption\|"Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz."]]
−
−
−	~~==List of Specifications==~~
−
−
−	*'''Activities''': Walking and running
−
−
−	*'''Accelerometer''':
−	**'''Placement of 3-axis Accelerometer''': Anywhere around the ankle in any orientation as shown in Figure 1.
−	**'''Sensitivity of the Accelerometer''': (+-) 4g or more. 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.]
−
−
−	*'''Input data''':
−	**accX - accelerometer data from X - axis
−	**accY - accelerometer data from Y - axis
−	**accZ - accelerometer data from Z - axis
−	**'''Input data format''': The accelerometer signals should be in '''units of m/s^2''' and need to be in .mat format [in Matlab file format].
−	**'''IMPORTANT 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 running the implementation.'''
−
−
−	~~==Implementation==~~
−
−
−
−	~~The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running.~~
−
−	~~[[File:figure_gaitEvents.png\|thumb\|center\|1000px\|caption\|"Figure 2: The magnitude of the resultant accelerometer signal along with the detected Heel-Strike and Toe-Off events"]]~~
−
−
−
−
−
−	~~<div style="font-size: 120%;">'''The MATLAB code can be found here''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]</div>~~
−
−
−	~~<code>'''[HS,TO] = SK_gedAlgo(accX,accY,accZ,Fs,winSizeFactor,implement_type);'''</code>~~
−
−
−	~~'''Input Arguments:'''~~
−
−	*accX, accY, accZ [unit: m/s^2]- signals obtained from each individual axis of 3-axes accelerometer
−	*Fs [unit: Hz] - sampling frequency of the acceleration signal. Originally developed for 128 Hz
−	*winSizeFactor - size of the running window. Default value = 3. Vary this size from 2 to 6 to get better results or if code crashes.
−	*implement_type - To detect the gait event, two implementations are provided. Default value: 'fit'
−	**'fit' - 2D Gaussian distribution fitting is done to estimate gait event (runs slow)
−	**'fast'- faster implementation to estimate the event. Warning: 'fast' implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.
−
−
−	~~'''Output Arguments:'''~~
−
−	*HS - vector containing sample numbers of Heel-Strike occurrences
−	*TO - vector containing sample numbers of Toe-Off occurrences
−
−
−
−
−	~~<div style="font-size: 160%;">'''References'''</div>~~
−	~~----------------------------------------------------~~
−
−
−	~~<div style="font-size: 120%;">'''Please remember to include an appropriate citation to acknowledge the use of library in all documents and papers that uses it:'''</div>~~
−	~~----------------------------------------------------~~
−
−
−	[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. 24, no~~. 12, pp.1363-1372, 2016~~
−
−	~~IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&newsearch=true&queryText=siddhartha%20khandelwal]~~
−	~~DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:909015]~~
−
−
−	[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: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:688909]~~
−
−
−	~~<div style="font-size: 120%;">'''You may also want to read:'''</div>~~
−
−	[3] Siddhartha Khandelwal, Nicholas Wickström, Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database, Gait & Posture, Volume 51, January 2017, Pages 84-90, ISSN 0966-6362, http://dx.doi.org/10.1016/j.gaitpost.2016.09.023.
−
−	~~Sciencedirect: [http://www.sciencedirect.com/science/article/pii/S0966636216305859]~~
−	~~Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2%3A999938&dswid=1416]~~
−
−	~~Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]~~
−
−
−
−	~~<div style="font-size: 160%;">'''Created By'''</div>~~
−	~~----------------------------------------------------~~
−
−
−
−	~~[http://islab.hh.se/mediawiki/Siddhartha_Khandelwal Siddhartha Khandelwal]~~

← Older revision		Revision as of 13:10, 12 December 2016
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−	[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	+	[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. 24, no. 12, pp.1363-1372, 2016

	IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&newsearch=true&queryText=siddhartha%20khandelwal]		IEEE url: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7423805&newsearch=true&queryText=siddhartha%20khandelwal]

@@ Line 53: / Line 53: @@
 *implement_type - To detect the gait event, two implementations are provided. Default value: 'fit'
 **'fit' - 2D Gaussian distribution fitting is done to estimate gait event (runs slow)
-**'fast'- faster implemention to estimate the event. Warning: 'fast' implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.
+**'fast'- faster implementation to estimate the event. Warning: 'fast' implementation might not give the best estimate of the gait event compared to gaussian fitting. It is not presented in the paper but is solely provided to estimate events faster without gaussian fitting.
 '''Output Arguments:'''
-*HS - vector containing sample numbers of Heel-Strike occurences
+*HS - vector containing sample numbers of Heel-Strike occurrences
-*TO - vector containing sample numbers of Toe-Off occurences
+*TO - vector containing sample numbers of Toe-Off occurrences

@@ Line 33: / Line 33: @@
 The implementation of the algorithm is provided as a library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running.
 <div style="font-size: 120%;">'''The MATLAB code can be found here''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]</div>
@@ Line 69: / Line 90: @@
 Diva: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2%3A999938&dswid=1416]

@@ Line 29: / Line 29: @@
 ==Implementation==
 The implementation of the algorithm is provided as a 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''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]
+<div style="font-size: 120%;">'''The MATLAB code can be found here''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]</div>
 Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]

← Older revision		Revision as of 15:18, 24 October 2016
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	'''The library can be found here''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]		'''The library can be found here''': [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]

−		+	Link to the MAREA gait database: [http://islab.hh.se/mediawiki/Gait_database MAREA gait databse]

@@ Line 3: / Line 3: @@
-This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running.
+Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.
-The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]
+[[File:accPlacement.jpg|right|thumb|200px|caption|"Figure 1: Position and orientation of each accelerometer at the beginning of each experiment. The accelerometer at the right ankle is attached arbitrarily without any pre-defined orientation. The FSRs are instrumented into the shoe soles to collect the ground truth. The data from all sensors is sampled at a frequency of 128 Hz."]]
-=='''List of Specifications'''==
+==List of Specifications==
-'''Placement of 3-axis Accelerometer''': Anywhere around the ankle in any orientation.
+*'''Activities''': Walking and running
-'''Sampling Frequency''': Preferred - 128 Hz [A Sampling frequency of 50Hz and above is acceptable. Please specify.]
+*'''Accelerometer''':
-'''Input data format''': abc.mat [Data in Matlab format.]
+*'''Input data''':
-<div style="font-size: 120%;">'''Citations'''</div>
 ----------------------------------------------------
 [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
@@ Line 37: / Line 55: @@
 DiVA url: [http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:688909]

@@ Line 20: / Line 20: @@
 '''Input data''': AccX, AccY, AccZ [Accelerometer signals from the X, Y and Z axes, respectively.]
-'''Input data format''': abc.mat or abc.csv [Data in Matlab format or comma separated text file.]
+'''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.'''''

← Older revision		Revision as of 14:22, 20 October 2016
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−	This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. The implemented algorithm is described in ''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 ''.	+	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: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]		The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]

@@ Line 2: / Line 2: @@
 == Gait Event Detection in Real-World Environments for Long-Term Applications ==
-Until this service is implemented, please send your data in the specified format to siddhartha.khandelwal@hh.se and nicholas.wickstrom@hh.se.
+This page provides the library that can be used to detect gait events from 3-axis accelerometer signals collected during walking or running. The implemented algorithm is described in ''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 ''.
-Specifications
+The library can be found here: [https://github.com/sidkha99/GaitEventDetection.git Link to Github repository]
 '''Activities''': Walking and running