The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.