Myo:Hauptseite - Versionsgeschichte

WikiSysop am 12. Juni 2019 um 10:32 Uhr

2019-06-12T10:32:48Z

2016-12-01T16:50:00Z

‎Project-Status

2016-11-29T17:06:56Z

‎Targets

2016-11-07T14:32:43Z

2016-11-07T14:32:21Z

‎Project-Team

2016-11-07T12:27:28Z

‎Project-Status

2016-11-07T12:23:41Z

‎Project-Status

2016-11-07T12:19:26Z

‎Description

2016-11-07T12:17:21Z

‎Project-Team

2016-11-03T14:15:05Z

‎Interal Documents

← Nächstältere Version		Version vom 12. Juni 2019, 10:32 Uhr
Zeile 21:		Zeile 21:
	* [[ Benutzer:a_sahu15 \| Amit Sahu ]]		* [[ Benutzer:a_sahu15 \| Amit Sahu ]]

−	* Kontakt: [mailto:swlab-myo@cs.uni-kl.de swlab-myo@cs.uni-kl.de]

	=Project-Status=		=Project-Status=

@@ Zeile 26: / Zeile 26: @@
 <!-- Versuchen Sie den aktuellen Projektstand für Außenstehende zu skizzieren. -->
-* We started with the task of digit recognition, once the band is worn by subject and a specific digit gesture is made. Once this task is completed, our assumption is the same model can be extended to alphabets and other complex gesture recognition.
+* We broke down the problem statement of gesture recognition to a controlled real-world simulation of a digit gesture recognition.
 * We have conducted experiments and collected data of 16 people so far. This data is further used for model generation and analysis for recognition of gestures based on specific feature set in each gesture.
-* For this we have utilized and analysed 4 classical machine learning approaches(Hidden Markov Models-HMM, Support Vector Machines-SVM, Naive Bayes-NB, K Nearest Neighbor-KNN) and Artificial Neural Networks approach(Long short term memory -LSTM).
+* For this we have utilized and analyzed 4 classical machine learning approaches(Hidden Markov Models-HMM, Support Vector Machines-SVM, Naive Bayes-NB, K Nearest Neighbor-KNN) and Artificial Neural Networks approach(Long short term memory -LSTM).
 * Created two sets of training instances
@@ Zeile 46: / Zeile 48: @@
 ** Andrews Curves
 * We have also developed and (open sourced) released an application for data visualization and capture for Myo armband. [https://github.com/sigvoiced/pewter https://github.com/sigvoiced/pewter]

@@ Zeile 6: / Zeile 6: @@
 =Targets=
 <!-- Listen Sie hier ihre kurzfristigen und langfristigen Ziele auf. -->
-* ..
+. To use Machine learning algorithm for identification of hand gestures.
-* ...
 = Project-Team =

@@ Zeile 20: / Zeile 20: @@
 * [[ Benutzer:a_sahu15 | Amit Sahu ]]
-* Kontakt: [mailto:swlab-myo swlab-myo]
+* Kontakt: [mailto:swlab-myo@cs.uni-kl.de swlab-myo@cs.uni-kl.de]
 =Project-Status=

@@ Zeile 19: / Zeile 19: @@
 * [[ Benutzer:a_dash15 | Ayushman Dash ]]
 * [[ Benutzer:a_sahu15 | Amit Sahu ]]
 =Project-Status=

@@ Zeile 23: / Zeile 23: @@
 <!-- Versuchen Sie den aktuellen Projektstand für Außenstehende zu skizzieren. -->
-* We started with the task of digit recognition, once the band is worn by subject and a specific digit gesture is made. Once this task is completed, our assumption is the same model can be extended to alphabets and other complex gesture recognition. ...
+* We started with the task of digit recognition, once the band is worn by subject and a specific digit gesture is made. Once this task is completed, our assumption is the same model can be extended to alphabets and other complex gesture recognition.
 * Created two sets of training instances
@@ Zeile 38: / Zeile 42: @@
 ** Parallel Coordinates
 ** Andrews Curves
 * Now we aim to wrap up these results via an application that is capable of capturing, analyzing (fixed set of)gestures in real time and classifying them. Once this is done we can give a live demo/presentation of our results so far and continue our work towards more complex and higher order gestures.

← Nächstältere Version		Version vom 7. November 2016, 12:23 Uhr
Zeile 25:		Zeile 25:
	* We started with the task of digit recognition, once the band is worn by subject and a specific digit gesture is made. Once this task is completed, our assumption is the same model can be extended to alphabets and other complex gesture recognition. ...		* We started with the task of digit recognition, once the band is worn by subject and a specific digit gesture is made. Once this task is completed, our assumption is the same model can be extended to alphabets and other complex gesture recognition. ...

		+	* Created two sets of training instances
		+	** One with 10 instances per class
		+	** One with 20 instances per class
		+	* Evaluated models using the following algorithms,
		+	** HMM - Raw Data
		+	** HMM - Windowed Features
		+	** Naive Bays
		+	** KNN (1 neighbour)
		+	** SVM (Parameters using grid search)
		+	* Analysed the accuracy precision, F-Score for all the models in all the folds
		+	* Analysed the features and tried to decide which features to eliminate and which features are not significant using.
		+	** Parallel Coordinates
		+	** Andrews Curves
		+
		+	* Now we aim to wrap up these results via an application that is capable of capturing, analyzing (fixed set of)gestures in real time and classifying them. Once this is done we can give a live demo/presentation of our results so far and continue our work towards more complex and higher order gestures.

	=Internal Documents=		=Internal Documents=

@@ Zeile 1: / Zeile 1: @@
 = Description =
 Hier entwickeln Master-Studierende eine Software, um mit einem speziellen Armband (MYO) eine Gestenerkennung für Gebärdensprache zu realisieren.
 =Targets=

@@ Zeile 14: / Zeile 14: @@
 -->
-* [[ Benutzer:??? | ??? ]]
+* [[ Benutzer:r_shringi15 | Rajveer Shringi ]]
 =Project-Status=

@@ Zeile 22: / Zeile 22: @@
-=Interal Documents=
+=Internal Documents=
 Die hier verlinkten weiteren Seiten zu diesem Projekt sind '''nur für angemeldete SWLab-Teilnehmer''' lesbar.
 <!-- Verlinken Sie hier interne Dokumente im Projekt-Namespace.