1. "EMG Based Decoding of Object Motion in Dexterous, In-Hand Manipulation Tasks" by A. Dwivedi, Y. Kwon, et. al.
    https://www.newdexterity.org/2018_BioRob_EMGDexterousManipulation.pdf
  2. "Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network" by Z. Zhang, K. Yang, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679304/
  3. "Machine Learning Algorithms for Characterization of EMG Signals" by Bekir Karlik , et. al.
    http://www.ijiee.org/papers/433-S3002.pdf
  4. "Simultaneous sEMG Classification of Hand/Wrist Gestures and Forces" by Francesca Leone, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6593108/
  5. "Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor" by Nadia Nasri, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359473/
  6. "Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques" by Muhammad Zia ur Rehman, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111443/
  7. "Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning" by Ulysse Cotˆ e-Allard, et. al.
    https://arxiv.org/pdf/1801.07756.pdf
  8. "Comparison of six electromyography acquisition setups on hand movement classification tasks" by Stefano Pizzolato, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638457/
  9. NinaPro Project Database by Stefano Pizzolato, et. al.
    http://ninaweb.hevs.ch/
  10. MyoText by Doug A. Bowman, et. al.
    https://research.cs.vt.edu/3di/node/231
  11. 15 Things You Can Do With Myo by Thalmic Labs
    https://medium.com/thalmic/15-things-you-can-do-with-myo-f3f05bcc6f9c
  12. "An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion" by Fei Wang, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603597/
  13. NinaPro Databse 5 Gesture Set by Stefano Pizzolato, et. al.
    https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=6515175_sensors-19-01952-g0A1.jpg
  14. "sEMG-Based Hand-Gesture Classification Using a Generative Flow Model" by Wentao Sun, et. al.
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515175/
  15. "Simple Space-Domain Features for Low-Resolution sEMG Pattern Recognition" by Ian M. Donovan, et. al.
    https://bidal.sfsu.edu/~kazokada/research/okada_embc17_myoFeature.pdf
  16. "A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition" by Ulysse Côté-Allard
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6631507/

Image Sources

  1. Tap Strap:
    https://cnet1.cbsistatic.com/img/00XQEmFzx7Xio51Kw8V0E4zo_oE=/2017/11/21/b97d2dc7-e471-47b8-a2e0-9091b2d26bcd/fl-tapkeyboard-cnetstill.jpg
  2. Keyboard Gestures:
    https://www.typing.academy/app/source/public/images/intro/en/basic-position.png
  3. Finger Tap:
    https://ak1.picdn.net/shutterstock/videos/13097291/thumb/3.jpg
  4. Finger Extension:
    https://www.online-tech-tips.com/wp-content/uploads/2018/10/touch-typing-tips.jpg.optimal.jpg
  5. Gesture Set:
    https://miro.medium.com/max/2604/1*9uvS5j1EZXdQuIoqyb5syA.jpeg
  6. Myo Armband:
    https://www.researchgate.net/profile/Eduardo_Castello/publication/316538856/figure/fig9/AS:487955379822600@1493349036712/Myo-armband-by-Thalmic-Labs.png
  7. Model Pipeline:
    https://www.mdpi.com/sensors/sensors-19-02495/article_deploy/html/images/sensors-19-02495-g003.png