osw_keeby

Samsung Tizen OS Application

My AI Persnol Trainer, πŸ”₯S-Coach !

Project : MachineLearning based Samsung Galaxy Gear Healthcare-App

The 9th OSS(Open Source Software) Grand Developers Challenge μ‚Όμ„±μ „μž κΈ°μ—…μ œμ•ˆκ³Όμ œ λ³Έμ„ μ§„μΆœ ν”„λ‘œμ νŠΈ

  • ML(Classifier) + Web(Backend, Server) + Smartwatch App(Frontend, Client)
    • πŸš€ This repo is part of the full project code.

URL

⭐ URL을 Ctrl + 마우슀쒌클릭 ν•˜λ©΄ μ™ΈλΆ€λ§ν¬λ‘œ μ΄λ™ν•©λ‹ˆλ‹€ πŸ˜€

λ‚΄μš© URL
1. λŒ€νšŒμ†Œκ°œ https://www.oss.kr/notice/show/6008d9bc-66f0-4373-a9df-19a8973c7038
2. μ‹œμ—°μ˜μƒ https://youtu.be/p5vPWqi1B6w
3. λ°œν‘œμžλ£Œ https://www.slideshare.net/SuHyunCho2/sws-56703648#1
4. κ°œλ°œλ¬Έμ„œ https://www.slideshare.net/secret/bsfNKp1uR5Y1q8
5. λŒ€νšŒ 이후 λ…Όλ¬Έμž‘μ„±ν•˜μ—¬ 기둝 https://www.slideshare.net/SuHyunCho2/recognition-of-anaerobic-based-on-machine-learning-using-smart-watch-sensor-data ([paper site1] / [paper site2])


S-coach

AI personal trainer App based on Machine Learning using Samsung tizen smart watch.

Note

  • μ‚Όμ„± 타이젠 OS 기반의 Gear(Gear2 , Gear S, Gear S2) μ• ν”Œλ¦¬μΌ€μ΄μ…˜
  • tizen-sdk-2.3.1
  • Device Optimization completed on samsung smartwatch version(Samsung Galaxy Gear 2, Gear S, Gear S2)
  • Tizen app type: Companion(Operating with Samsung Galaxy S4(android 4.4))

Project Summary

λ¨Έμ‹ λŸ¬λ‹ 기반 인곡지λŠ₯ ν”ΌνŠΈλ‹ˆμŠ€ ν—¬μŠ€ μ½”μΉ˜ μ–΄ν”Œλ¦¬μΌ€μ΄μ…˜.
3μΆ•κ°€μ†λ„μ„Όμ„œ 3μΆ•μžμ΄λ‘œμŠ€μ½”ν”„μ„Όμ„œλ₯Ό 기반으둜 μ‚¬μš©μžμ˜ λͺ¨μ…˜μ„ μ‹€μ‹œκ°„ νŠΈλž˜ν‚Ή 및 μŠ€μΌ€μ₯΄λ§ν•˜μ—¬ ‘λ¬΄μŠ¨μš΄λ™’을 ‘λͺ‡νšŒ’ν–ˆλŠ”μ§€ 그리고 ‘칼둜리 μ†Œλͺ¨λŸ‰’κΉŒμ§€ 슀슀둜 μ•Œμ•„μ„œ νŒλ‹¨ν•˜κ³  κΈ°λ‘ν•˜μ—¬ 관리.
λ˜ν•œ μ‚¬μš©μžκ°€ μš΄λ™μ„ μ‹œμž‘ν•œ ν›„ μ‹¬λ°•μˆ˜λ₯Ό μ˜ˆμƒν•˜μ—¬ μš΄λ™μ„ μ΄‰μ§„ν• μˆ˜μžˆλ„λ‘ μ‚¬μš©μžμ˜ μ˜ˆμƒλœ 평균심μž₯λ°•λ™μˆ˜μ™€ κ°€μž₯ λΉ„μŠ·ν•œ BPM에 ν•΄λ‹Ήλ˜λŠ” μŒμ•…μ„ 슀슀둜 μ°Ύκ³  μ•Œμ•„μ„œ μž¬μƒ.

About Train Models(optimized)

  • Performance(Accuracy): about 96.7% for unseen data [2016. 10]
  • Model type: Discriminative Model ( P ( y | X ) ) for inference
  • Learning Type: Classification on Supervised Learning.
  • Using Dimension Reduction Skills e.g. PCA, LDA(Fisher’s LDA)
  • Using Kernel Tricks e.g. linear and rbf
  • Hybrid Stacking Model based on SVM(Support Vector Machine) Framework and others

About Project Enviroments

  • client side
    • python 3.4 / 2.7
    • tizen 2.3.1
    • java 8
    • android 4.4
    • windows 7
  • server side
    • ubuntu 14
    • AWS EC2 free tier
    • flask 0.9
    • nginx 1.4.6
    • mariadb 5.5.44
    • uwsgi 1.9.17.1
    • sqlalchemy 0.15

Reference

Visit original content creator repository
https://github.com/humblem2/osw_keeby

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