Update README.md

Python_version/README.md

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-# Real-Time-Rehabilitation
-Real time rehabilitation assessment with kinect v2. We implement the spatio temporal graph convolutional neural network for rehabilitation exercise assessment according to the following paper:  S. Deb, M. F. Islam, S. Rahman and S. Rahman, "[Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9709340)," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 410-419, 2022, doi: 10.1109/TNSRE.2022.3150392.
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-# How to run this code
-First and foremost you need the Microsoft Kinect v2 and install the associated dependencies. After that follow these steps:
-1) Install requirement.txt file
-2) Download the model parameters from the [google drive](https://drive.google.com/drive/u/1/folders/1c2Nucl8iIFhDvPZUjdkTFYoksaX1TpK_) and store them to best model folder
-3) Run PyKinectBodyGame_v1.py file
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-# Prediction
-Output for the healthy person. The correctness score is shown in real time at the top middle of the screen. Consequently, exercise name is shown at the top left.
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-https://github.com/SwaksharDeb/Real-Time-Rehabilitation/assets/49334830/a5dcd42b-e1fa-4ea4-902a-f7c19e5dd205
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-https://github.com/SwaksharDeb/Real-Time-Rehabilitation/assets/49334830/31c4520c-9886-4c9d-b7f2-0f31254387d8
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-Output for the patient. The correctness score is shown in real time at the top middle of the screen. Consequently, exercise name is shown at the top right.
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-https://github.com/SwaksharDeb/Real-Time-Rehabilitation/assets/49334830/46edbbff-5924-47e0-a190-89c740976a8e
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-https://github.com/SwaksharDeb/Real-Time-Rehabilitation/assets/49334830/b5153988-c712-484c-9b68-34578a748fbb
+# Kinectv2 Data Capture using Python (for multiple sensors)
 
+We will add detail soon...