https://github.com/akimach/gestureai

RNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures.
https://github.com/akimach/gestureai

coreml dataset deep-learning deep-neural-networks demo ios keras keras-models keras-tensorflow machine-learning python python-2 recurrent-nerural-network rnn tensorflow

Last synced: 2 days ago
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RNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures.

• Host: GitHub
• URL: https://github.com/akimach/gestureai
• Owner: akimach
• License: mit
• Created: 2017-09-25T10:43:24.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-09-28T08:37:14.000Z (about 7 years ago)
• Last Synced: 2024-10-01T20:34:26.788Z (2 days ago)
• Topics: coreml, dataset, deep-learning, deep-neural-networks, demo, ios, keras, keras-models, keras-tensorflow, machine-learning, python, python-2, recurrent-nerural-network, rnn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 4.89 MB
• Stars: 24
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GestureAI

[![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENSE)

`GestureAI` is a RNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures(Circl, Rectangle, Triangle, Cross and the other). This dataset of hand-motion drawing 5 figures is sequences of 3-axis accelerations captured by iPhone. Example to implement RNN in Keras gets 90.8% accuracy by Cross-validation.

## Demo

Trained Neural Network deployed on [GestureAI-iOS, iOS 11 app using CoreML](https://github.com/akimach/GestureAI-iOS) :

![demo](images/demo.gif)

## Get the Dataset

You can use direct links to download the dataset.

|Name|Examples|Size|Link|MD5 Checksum|

|:-:|:-:|:-:|:-:|:-:|

|`gesture-3axis-accel.tar.gz`|1,000|338 KBytes|[Download](https://github.com/akimach/GestureAI/blob/master/datasets/gesture-3axis-accel.tar.gz?raw=true)|`37664771fd60e930033fb24387fb1601 `|

## Labels

The dataset consists of 1,000 3-axis acceleration sequences of 5 gesture classes, which are defined by motions drawing 5 figures. We don't set a specific rule about stroke order for drawing a figure by hand.

|Label|Description|Examples|Figure|

|:-:|:-:|:-:|:-:|

|0|Circle|200|![Circle](images/circle.jpg)|

|1|Rectangle|200|![Rectangle](images/rectangle.jpg)|

|2|Triangle|200|![Triangle](images/triangle.jpg)|

|3|Cross|200|![Cross](images/cross.jpg)|

|4|Other|200||

## Requirement

* Python (2.7+)

* numpy (1.12.1+)

* protobuf (3.1.0+)

* Keras (1.2.2)

* TensorFlow (1.2.1)

* Scikit-learn (0.15+)

* coremltools (0.6.3)

## Install

```

$ virtualenv venv

$ source venv/bin/activate

$ git clone https://github.com/akimach/GestureAI.git

$ cd GestureAI

$ pip install -r requirements.txt

```

## Examples

Try with Jupyter notebook!

* Loading datasets

* Tuning hyper-parameters with Grid Search

* Training RNN with Early-stopping

## Licence

[MIT](https://github.com/akimach/GestureAI/blob/master/LICENSE)

## Author

[Akimasa KIMURA](https://github.com/akimach)