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README.md

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+---
+language: en
+license: mit
+tags:
+  - tensorflow
+  - tflite
+  - time-series
+  - activity-recognition
+  - imu
+  - edge-deployment
+  - pruning
+  - quantization
+datasets:
+  - uci-har
+metrics:
+  - accuracy
+---
+
+# IMU Activity Classifier — Pruning + INT8 Quantization
+
+Compact 1D-CNN for human activity recognition from 6-axis IMU signals.
+Trained on UCI HAR dataset, compressed via magnitude pruning (78% sparsity)
++ INT8 quantization for edge/microcontroller deployment.
+
+## Results
+
+| Model | Size | Accuracy | Latency |
+|-------|------|----------|---------|
+| Baseline FP32 | 358 KB | 93.99% | 46.1 ms |
+| Pruned FP16 TFLite | 191 KB | 92.40% | 0.054 ms |
+| **Pruned INT8 TFLite** | **113 KB** | **92.43%** | **0.026 ms** |
+
+- Size reduction: 68.4%
+- Latency speedup: 1775x
+- Accuracy drop: 1.56%
+
+## Classes
+WALKING · WALKING_UPSTAIRS · WALKING_DOWNSTAIRS · SITTING · STANDING · LAYING
+
+## Compression Pipeline
+1. Baseline 1D-CNN trained on UCI HAR (93.99% accuracy)
+2. Magnitude pruning with PolynomialDecay → 78% sparsity
+3. INT8 post-training quantization → 113KB TFLite model
+
+## Confusion Matrix
+![Confusion Matrix](confusion_matrix.png)
+
+## Compression Summary
+![Compression Summary](compression_summary.png)
+
+## Training Curves
+![Training Curves](training_curves.png)
+
+## Usage
+```python
+import tensorflow as tf
+import numpy as np
+
+interpreter = tf.lite.Interpreter("imu_pruned_int8.tflite")
+interpreter.allocate_tensors()
+input_det  = interpreter.get_input_details()
+output_det = interpreter.get_output_details()
+
+# sample shape: (1, 128, 9) — float32
+sample = np.random.randn(1, 128, 9).astype(np.float32)
+interpreter.set_tensor(input_det[0]['index'], sample)
+interpreter.invoke()
+output = interpreter.get_tensor(output_det[0]['index'])
+
+ACTIVITIES = ['WALKING','WALKING_UPSTAIRS','WALKING_DOWNSTAIRS',
+              'SITTING','STANDING','LAYING']
+print(ACTIVITIES[np.argmax(output)])
+```
+
+## Links
+- GitHub: https://github.com/RAj5517/imu_activity_classifier
+- Dataset: UCI HAR (University of California Irvine)