Classifying 6 daily activities from smartphone accelerometer and gyroscope signals. UCI HAR Dataset — 30 subjects, Samsung Galaxy S II, 50 Hz, 561 pre-extracted features. Best model: 95.52% accuracy. Live phone demo runs the ML model entirely in your browser.
30 volunteers aged 19–48 performed 6 activities wearing a Samsung Galaxy S II at the waist. Signals captured at 50 Hz, segmented into 2.56 s windows with 50% overlap.
All models evaluated on held-out test subjects (9 of 30 subjects never seen during training).
What the data reveals about activity recognition using inertial sensors.
Gravity acts along a completely different axis when horizontal. The gravity acceleration along Z becomes dominant — completely separating LAYING from all other activities. 100% precision and recall on the test set.
Both are static postures with similar gravity magnitude. The only difference is a subtle change in trunk angle. ~48 SITTING windows misclassified as STANDING in the confusion matrix — the hardest confusion pair.
Gravity features dominate — they encode body orientation and effectively separate static from dynamic activities.
End-to-end flow from raw sensor signals to activity prediction.
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. ESANN 2013, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
↗ UCI ML Repository — Human Activity RecognitionOpen the Streamlit app on your phone. The ML model runs entirely in your browser — no data leaves your device. Tap Start, move naturally, see your activity predicted in real time.