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