feat: this PR adds blog for EMG Armband. (#50)

* added code walkthorugh of the blog

* added git repo

* added bloogh

* fix typos

* fixed bg

* bg fix wont work

* removedf the bg

* Update 2024-07-26-how-does-emg-armband-work.md

* added final things

* fixed the .ds_store issue, fml

* added updates on *weekly* basis

* added lore about ml and esp_now protocall

* revert changes

_posts/2024-07-26-how-does-emg-armband-work.md

@@ -0,0 +1,145 @@
+---
+layout: post
+title: EMG Armband, The Conclusion
+tags: ML Systems IoT PCB Designing Embedded
+description: Developing an EMG system that can understand human hand motions.
+---
+
+## Mentees:
+- [Shaunak Datar](https://github.com/ShaunakKDatar)
+- [Piyush Patle](https://github.com/PiyushPatle26)
+- [Atharva Kshirsagar](https://github.com/vovw)
+
+### This project is sponsored by [Upside Down Labs](https://upsidedownlabs.tech/)
+![up](/assets/posts/emg-armband/upside_down.svg)
+### GitHub Repo: [vovw/emgband](https://github.com/vovw/emgband)
+
+# What is our project?
+We read Electromyography (EMG) data using sensors, amplify it, filter it, perform ADC conversion, and conduct a frequency analysis (FFT) on it. Using the analyzed data, we train an ML model that can understand what hand movement was performed to get those readings.
+
+![Hardware Flow](/assets/posts/emg-armband/hardware_flow.png)
+
+# What work has been done?
+- Initially, we *attempted* to approach this problem from first principles. We implemented the **biopotential signal-acquisition board** from scratch but encountered some issues.
+- We then pivoted temporarily to using Upside Down Labs' [EXG Pill](https://github.com/upsidedownlabs/BioAmp-EXG-Pill), which works well.
+(Add images of the readings)
+
+## Signal Processing:
+- The raw signal goes through a bandpass filter to focus on frequencies between 75 and 150 Hz.
+- This filtering helps remove noise and focus on the most relevant part of the EMG signal.
+
+## ADC (Analog-to-Digital Converter)
+- Converts the continuous analog signal from the sensor into digital values the microcontroller can process.
+- We utilized the entire 12-bit ADC provided in the ESP32.
+- We later bound the values and mapped them to reduce the 0-4095 range to 0-1000 readings.
+
+## Bandpass Filter:
+- A filter that allows signals between two specific frequencies to pass through while blocking others.
+- A bandpass filter which filters the signal and returns a 72.5-725Hz signal is included in the Bio-amp EXG Pill.
+- Additionally, we implemented a Band-Pass Butterworth IIR digital filter generated using filter_gen.py.
+
+## Frequency Analysis:
+- The code uses the Fast Fourier Transform (FFT) to convert the time-domain signal into frequency components.
+- This helps identify which frequencies are most prominent in the EMG signal.
+- We used the [Cooley-Tukey Algorithm](https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm) for the Fast Fourier Transform.
+
+![FFT](/assets/posts/emg-armband/fft.png)
+
+## ESP-now
+
+WIP code implimentation - [vovw/emgband](https://github.com/vovw/emgband/commit/8a69945c57ba3d73bbc88cc48f8bd2ccd03453a0)
+
+1. **ESP-NOW Sender Configuration**:
+   - The sender code initializes ESP-NOW and sets up a task to periodically send sensor data (ADC readings) to a broadcast address.
+   - The sensor data is read from a GPIO pin using the ADC module.
+   - The sender registers a peer with a broadcast address and sends data using `esp_now_send`.
+
+2. **ESP-NOW Receiver Configuration**:
+   - The receiver code initializes ESP-NOW and registers a callback function to handle incoming data.
+   - The receiver adds a peer with a specific MAC address (sender's address) to receive data.
+   - The received data is written to a file in the SPIFFS filesystem for storage and further processing.
+
+3. **File System Integration**:
+   - The receiver uses the LittleFS file system to store the received data in a file named `output.txt`.
+   - The file system is initialized and a file is created to store the incoming data.
+
+We have successfully implemented ESP-NOW communication between two ESP32 devices. The sender device reads ADC data from a GPIO pin and sends it to the receiver using ESP-NOW. The receiver device receives the data and stores it in a file on the LittleFS file system.
+
+#### here is the complete flow of how it works
+![Flow](/assets/posts/emg-armband/flow.png)
+
+## Storing the Signal
+To train our Machine Learning Model, we need to store the readings as data. We initially decided to label two events: Finger Moving and Palm Closing.
+
+We output the data in frequency bins generated by the FFT on the serial port and store it in a .txt file. We wrote a Python script that maps this data from .txt to a CSV format, making it easier to read using pandas in our Model script.
+
+## Machine learning
+[vovw/emgband/ml](https://github.com/vovw/emgband/tree/main/ml)
+
+We are currently expirementing with different architecture mainly a simple [feed forward network](https://en.wikipedia.org/wiki/Feedforward_neural_network) and [classfication](https://en.wikipedia.org/wiki/Statistical_classification). Depending on the loss curves and which architecture fits the traning data better we will decide to go with that one.
+
+#### planned ml ML for the EMG system
+![ml](/assets/posts/emg-armband/ml.png)
+
+
+# Updates on weekly basis
+
+## Week 1: Fundamentals and Electronic Components
+- Gained understanding of operational amplifiers
+- Studied bandpass filters
+- Explored essential electronic components for bio-amplifiers
+- Introduced to basic neural networks and their implementation
+
+## Week 2: Hardware Acquisition and Signal Understanding
+- Received components from Upside Down Labs
+- Focused on understanding EMG bio-amplifier output signals, specifically the EXG Pill
+- Studied open-source documentation and repositories
+- Experimented with basic machine learning papers and algorithms
+
+## Week 3: Signal Processing and Machine Learning Foundations
+- Implemented Fast Fourier Transform (FFT) code
+- Worked on Analog-to-Digital Conversion (ADC)
+- Adapted Arduino code for ESP32 platform
+- Studied optimization algorithms:
+  - Gradient descent
+  - Backpropagation
+
+## Week 4: Data Collection and Wireless Communication
+- Developed Python script for:
+  - Data collection
+  - Data cleaning
+  - CSV storage
+- Implemented ESP-NOW protocol for communication between two ESP32 devices:
+  - One connected to the computer
+  - One connected to the hand band
+- Progress towards creating a wireless EMG band
+
+The following weeks dictate the project flow:
+
+## Week 5: Comprehensive Data Collection
+- Gather data for various hand movements:
+  - Individual finger movements
+  - Clenching and relaxing wrists
+  - Thumbs up and thumbs down gestures
+- Focus on data cleaning and preparation
+
+![hand](/assets/posts/emg-armband/claw.png)
+
+## Week 6: Model Development and Embedded Implementation
+- Create a sequential model
+- Train the model on collected data
+- Experiment with different architectures to optimize output
+- Convert the trained model to TensorFlow Lite format
+- Run the model locally on ESP32
+
+## Week 7: Hardware Integration and Enclosure
+- Design and create hardware casing to enclose:
+  - Three Upside Down Labs Patchy devices
+  - Amplifiers
+  - ESP32 microcontroller
+- Focus on developing a clean and efficient system
+![emg](/assets/posts/emg-armband/emg.png)
+
+## Week 8: Web Integration and Future Applications
+- Develop a web server for data visualization and control
+- Explore potential applications of our EMG system