guillaume-chevalier/LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
Star & Fork Trend (35 data points)
Multi-Source Signals
Growth Velocity
guillaume-chevalier/LSTM-Human-Activity-Recognition has -1 stars this period . Velocity data will be available after more historical data is collected.
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| Metric | LSTM-Human-Activity-Recognition | SiamMask | ml-workspace | hummingbird |
|---|---|---|---|---|
| Stars | 3.5k | 3.5k | 3.5k | 3.5k |
| Forks | 936 | 809 | 456 | 290 |
| Weekly Growth | -1 | +0 | +0 | +1 |
| Language | Jupyter Notebook | Python | Jupyter Notebook | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | MIT | Apache-2.0 | MIT |
Capability Radar vs SiamMask
Last code push 1249 days ago.
Fork-to-star ratio: 26.4%. Active community forking and contributing.
Issue data not yet available.
No measurable growth in the current period (first-day cold start expected).
Licensed under MIT. Permissive — safe for commercial use.
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