---
tags:
  - deep-learning
  - lstm
  - human-activity-recognition
  - sensor-data
license: mit
library_name: keras
---

# Human Activity Recognition with LSTM

## Overview
This project focuses on **Human Activity Recognition (HAR)** using **LSTM-based neural networks**. The goal is to classify different human activities based on motion sensor data.

### Dataset Used
The model is trained on the **UCI HAR Dataset**, a widely used benchmark dataset for human activity recognition. It contains data collected from accelerometers and gyroscopes of smartphones while subjects performed daily activities.

## Model Performance
### Classification Report
Below are the precision, recall, and F1-score for each activity class:


```plaintext
              precision    recall  f1-score   support

     Class 0       0.92      0.98      0.95       496
     Class 1       0.95      0.91      0.93       471
     Class 2       0.98      0.95      0.96       420
     Class 3       0.92      0.94      0.93       491
     Class 4       0.94      0.93      0.94       532
     Class 5       1.00      0.99      1.00       537

    accuracy                           0.95      2947
   macro avg       0.95      0.95      0.95      2947
weighted avg       0.95      0.95      0.95      2947

 ```

### Confusion Matrix
The confusion matrix below visualizes the model's performance in classifying different activities:

![Confusion Matrix](https://huggingface.co/Mic52/Human-Activity-Recognition/resolve/main/Figure_1.png)

## Next Steps
- Improve the model with **GRU & CNN architectures**.
- Expand testing with **real-world sensor data**.
- Fine-tune hyperparameters for better generalization.