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poppy-kine:poppy-kine-2016-s4 [2016/06/20 20:31]
poppy-kine:poppy-kine-2016-s4 [2019/04/25 14:08]
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-{{tag>​Poppy Project}} 
-====== Poppy-Kine : S4 project 2015-2016 ====== 
-===== Introduction ===== 
-This project is organised under the "​Projet d'​ingĂ©nieur"​ of Telecom BRETAGNE for the scholar year 2015-2016. Its main objective is to evaluate the performance of the patients in their reeducation exercises and to give the feedbacks by a humanoid robot based on a text-to-speech system. The robot will be used is **Poppy** (see [[http://​poppy-project.org/​|Poppy project]] and [[poppy|Poppy robot wiki page]]), the movements are captured by the **camera Kinect** (v2). 
-\\ For the recorded and reproduction parts, we have used the work of projet S5 (see [[http://​gaudi.enstb.org/​doku.php?​id=poppy-kine-2015-s5 | poppy-kine-2015-s5]]). 
-\\ We implemented the filtering in order to have the proper movements without noise of the patients. This will help us to build the **GMM model** to compare the movements of the patients with the reference ones of the kinesitherapist. After having the resultat of evaluation, we used the **MaryTTS** (see [[http://​gaudi.enstb.org/​doku.php?​id=marytts | marytts]]) to give the oral feedbacks. 
-===== Our works ===== 
-  - First step: getting the data from the camera Kinect, see the clear instruction at [[http://​gaudi.enstb.org/​doku.php?​id=poppy-kine-2015-s5 | poppy-kine-2015-s5]] 
-  - Second step: filter the data, after the first step, we will have the movements'​ data in form of <​name_of_the_exercise_x>​.txt file (x starts from zero (first movement)). The Butterworth filter is used in order to obtain the proper movements without noise. 
-  - Third step: detection of the beginning and the end of the movements. After that building the GMM model in MATLAB. 
-==== First step: getting the data ==== 
-==== Second step: filter the data ==== 
-We used a low pass filter to decrease the effects of the noise. In this case, we decided to choose the filter Butterworth because of its advantage which is its formula of the transfer function is the same for all the orders: 
-After considering the performance of the filter with several orders, we chose //n=5// for our filter. The figure below shows the filtered data for the joint **//"​Head"//​** correspond to the skeleton of the camera Kinect. 
-{{:​filter_butterworth.png?​| The filtered data with filter Butterworth}} 
-==== Third step: detection of the beginning and the end of the movements ==== 
-=== Detection of the beginning === 
-To detect the beginning of the movement, we supposed that the noise always has the amplitude which is smaller than the movements'​ amplitudes. We have calculated an interval outside of which the signal can be considered as the movements. 
-\\ This figure below presents the detection of the beginning for the movements of the joint **//"​Hip-Left"//​**. 
-{{:​detection_beginning.png?​direct|Detection of the beginning}} 
-=== Detection of the end of the movements & separation the cycles ==== 
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