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---
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tags:
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- deep-learning
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- lstm
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- human-activity-recognition
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- sensor-data
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license: mit
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library_name: keras
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---
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# Human Activity Recognition with LSTM
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## Overview
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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.
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### Dataset Used
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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.
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## Model Performance
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### Classification Report
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Below are the precision, recall, and F1-score for each activity class:
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```plaintext
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precision recall f1-score support
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Class 0 0.92 0.98 0.95 496
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Class 1 0.95 0.91 0.93 471
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Class 2 0.98 0.95 0.96 420
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Class 3 0.92 0.94 0.93 491
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Class 4 0.94 0.93 0.94 532
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Class 5 1.00 0.99 1.00 537
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accuracy 0.95 2947
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macro avg 0.95 0.95 0.95 2947
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weighted avg 0.95 0.95 0.95 2947
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```
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### Confusion Matrix
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The confusion matrix below visualizes the model's performance in classifying different activities:
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## Next Steps
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- Improve the model with **GRU & CNN architectures**.
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- Expand testing with **real-world sensor data**.
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- Fine-tune hyperparameters for better generalization.
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