Human-Activity-Recognition-using-MutliHead-Convolutional-Attention

Table of Contents

• Introduction
  • Installation
• Dataset
• Model Architecture
• Training
• Quantization
• Results
• References

Introduction

This repository implements a multi-head convolutional attention-based model for human activity recognition. The model employs attention mechanisms to improve recognition accuracy by focusing on informative regions in the input data. Further, the obtained model is Quantized for supporting IoT-based devices.

Installation

1. Clone the repository:

   git clone https://github.com/Arshdeep-Singh-01/Human-Activity-Recogonition-using-MutliHead-Convolutional-Attention.git

2. Navigate to the project directory:

   cd Human-Activity-Recogonition-using-MutliHead-Convolutional-Attention

3. Install the required dependencies:

   pip install -r requirements.txt

Dataset

This task use the WISDM dataset which contains the acceleration values (x,y,z) and the corresponding activity, along with the temporal components Dataset is freely avaliable at WISDM Train Data: 80% Test Data: 20%

Model Architecture

The overview of the model architecture is as follows:

• 3-Head Convolutional Neural Network
• Concatination followed by Maxpooling
• 30-Head Convolutional Attention
• Fully connected dense layers

Training

The model was trained over 200 epochs (20+ hours of computation)

• Optimizer: SGD(lr = 0.001)
• Loss: Cross Entropy Loss

Quantization

The trained model was then Quantized using Post Training Dynamic Quantization

Model	Size (MB)
Original Model	373.094
Quantized Model	0.009

Results:

Activity	Accuracy (Original Model)	Accuracy (Quantized Model)
Walking	98.06%	97.86%
Jogging	99.82%	98.96%
Upstairs	92.87%	92.57%
Downstairs	98.5%	98.11%
Sitting	95.05%	93.85%
Standing	99.65%	98.97%
Overall	97.33%	96.72%

References

1. Attention Is All You Need
2. A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention
3. Post Training Dynamic Quantization