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# System prepended metadata

title: Capstone Meeting - 20/09/2020

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# Capstone Meeting - 20/09/2020

* Assessment review (Poster, Endeavour Presentation Video):
    * due - first week of the october;
    * [graded] poster - Friday, 9th October; [ref] https://endeavour.unimelb.edu.au/students/workshops
    * [not graded] endeavour presentation; - Friday, 9th October
* Finalise Objectives for our project, and  Specifications of our System:
* Project updates
    * Setting what needs to be done and what is out of scope
    * Documentation

## Assestment Review
![](https://files.slack.com/files-pri/TUTMCKWJ1-F019Q0S024V/engr90037-38_subject_overview-3.png)
* Poster [Link](https://www.overleaf.com/project/5f4cecfe2b40f00001f23535)
* due - first week of the october;
* [graded] poster - Friday, 9th October; [ref] https://endeavour.unimelb.edu.au/students/workshops
* [not graded] endeavour presentation; - Friday, 9th October

## Objectives & Specifications

#### Project Objectives:
* Translate emergency auslan signs in real-time;
* Bring forth the awareness of the auslan community towards the public;
* [KIV] - building an application on top of real-time pose estimation; (skeleton based);
* [KIV] Integrate our knowledge in DSP and and machine learning to build a real-world application? 
* [KIV] Showcasing signal processing techniques and machine learning in building a real-world application?


#### System Specifications
* System successfully shows a proof of concept of recognising signs using human pose estimation.
* Technical Specifications/Metrics
    * Accuracy:
        * [Notes]  - Applying it on validation/test set, real world 
        * Quantity - How many people successfully picked up? generalization;
        * Quality  - How well does it pick up? 
    * Speed:
        * Factors
            * Internet Connection Quality - CISCO VPN, WiFi, User's Internet.
            * Computation Limit - GPU Specs on Cloud/Desktop's GPU power to do inferencing.
        * [Notes]:
            * Let's set an average internet connection rate to define our system speed based on **INTERNET CONNECTION**
            * We can test out system on a few different systems to see how well it performs to infer signs.
        
* Number of signs
    * Able to recognise up to 5 UNIQUE Auslan signs continuously.
        * As you increase sign classes, training data increases
        * [flag - report] Highlight limitation
    * isolated signs
        
## Project Updates 

### Frame reduction
* Reducing sampling rate from 75 to 35.
    * Automated into datapipeline.
* Steps:
    1) Cut from 75 to 70 first.
    2) Exclude frames that have zero keypoints.
    3) if you still have 70 frames; 
        * Get odd and even sample frames.
* Doubled dataset using both odd and even frames as data.

### Sign Updates
* Hospital, Ambulance, Pain, Thumbs Up, Help.
* Increase variability by recording different data.

### Application Updates
* Test by ONLY reducing window width.
* Re-deploy using WebRTC

## Moving Forward
* Evaluation:
    * Read up/Research on valid metrics that we can use in our project to define specs (accuracy); (Yick and Tsz Kiu)
    * speed (Matthew and Yick)
* Poster & Presentation:
    * Poster (Tsz Kiu and Matthew)
    * Presentation Slides - Matthew
    * Meet up middle of next week to discuss.
* research limitation;
    * read up on exploding gradient vs vanishing gradient;
    
* [yick]
    * batch generator; 
    * hyperparameter with spark
        * [tune] number of hidden units:
            * lstm layer;
            * Dense Layer Numbers
        * [tune] Dropout rate:
            * discretize to fix within [0.1, 0.9];
        * Learning Rate
            * Default value is quite ok
        * Decay Rate
            * default value is quite ok;
        * Optimizer - Adam
    * augmentation,
    * compensation;
    * tweak variability;
* [Matt]
    * Port to WebRTC to test speed with old system.
    * Test out new model.



