Prototype Development: 2024-25

1. Indoor Activity Detection and Personal Health Assistance Using Pollution Sensors on a TinyML Board 

• • • Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India: https://dl.acm.org/doi/full/10.1145/3685694

    • Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting: https://dl.acm.org/doi/abs/10.1145/3640471.3680243

    • Indoor Air Quality Dataset, Dalton: https://iitkgpacin-my.sharepoint.com/personal/pkarmakar_kgpian_iitkgp_ac_in/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fpkarmakar%5Fkgpian%5Fiitkgp%5Fac%5Fin%2FDocuments%2FDALTON%5FDataset%5FNeurIPS2024%2Fdalton%2Ddataset%2Dfiles%2Ezip&parent=%2Fpersonal%2Fpkarmakar%5Fkgpian%5Fiitkgp%5Fac%5Fin%2FDocuments%2FDALTON%5FDataset%5FNeurIPS2024&ga=1

    • AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer https://arxiv.org/abs/2204.11484

    • Github Code: https://github.com/prasenjit52282/AQuaMoHo/tree/master

    • Efficient Air Quality Index Prediction on Resource-Constrained Devices using TinyML: Design, Implementation, and Evaluation: https://drive.google.com/file/d/1jmtHQFOSUN7YcLuU33-QTvocgkPTe4ok/view?usp=sharing

    • Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics: https://arxiv.org/abs/2310.12241

    • TinyML on-device neural network training : https://drive.google.com/file/d/1ryTFHdZpyu0gu9Sm2D3bIpZTP99mAEKv/view?usp=sharing

    • Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq

    • Calculating an actionable indoor air quality index: https://www.breeze-technologies.de/blog/calculating-an-actionable-indoor-air-quality-index/

2.  Head Gesture Recognition  and Control YouTube or Facebook using said gestures in  Raspberry Pi Zero 2 w or Raspberry Pi 5

• Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control - https://ieeexplore.ieee.org/document/9810969

• HGaze Typing: Head-Gesture Assisted Gaze Typing: https://dl.acm.org/doi/10.1145/3448017.3457379

• Github link: head-tracking-gesture-recognition - https://github.com/cohnt/head-tracking-gesture-recognition

3.  Assistive Living using  Sound Sensor & mmwave Sensor in TinyML Board  or Raspberry Pi Zero 2 w

• Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices: https://www.sciencedirect.com/science/article/pii/S0031320322005052

• Github Code: https://github.com/mohaimenz/acdnet

• Passive Monitoring of Dangerous Driving Behaviors Using mmWave Radar: https://www.sciencedirect.com/science/article/pii/S1574119224000750

• mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving: https://ieeexplore.ieee.org/abstract/document/10099264/

4.  Development of Smart Shelf using Capacitive Sensor 

• CAPACITIVE BASICS: https://youtube.com/playlist?list=PLB_UXCAgjFGq400zWa_V41lvzIFGGwu98&si=Pe0oM_9Q-TQRaKdW

• TOUCHE, SFCS: https://la.disneyresearch.com/wp-content/uploads/touchechi20121.pdf

• SFCS OPEN SOURCE APPLICATION: https://www.nime.org/proceedings/2014/nime2014_515.pdf

• PURCHASE: 

  • https://robu.in/product/raspberry-pi-pico-2/

  • https://robu.in/product/ad9850-dds-signal-generator-module/

Notes

1. Study KD Trees (You'll Understand WHY Decision Trees) from here: https://youtu.be/BzHJ57QCdVo?si=HdfaG4XSPvNyiMo6
2. Decision Trees: https://youtu.be/E1_WCdUAtyE?si=OKgHO3dnTD6-4GHV
3. Random Forests: https://youtu.be/4EOCQJgqAOY?si=fdzpmQoPLjCVpzza
4. Applying Decision Trees: https://scikit-learn.org/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html
5. Applying Random Forests: https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html
6. Applying Artificial Neural Networks: https://www.tensorflow.org/tutorials/quickstart/beginner
7. Deep Learning Course: https://youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb&si=vO12FED7ha0Qc2Iu