Classification of human activity recognition using smartphones

Detecting individual activity on smartphones still seems to be a challenge given the limitations of resources such as battery life and computational workload capacity. Considering user activity and managing them, we can conceive low power consumption for mobile phones and other mobile devices, which requires a complete and rigorous program to recognize a ctivities and adjust device power consumption regarding their application at different times and places. However, with the rapid development of new and innovative applications for mobile devices such as smartphones, advances in battery technology do not ke ep up, especially in energy conservation. On the other hand, the use of activity recognition is increasing in active and preventive healthcare applications at home, learning environments of security systems, and a variety of human - computer interactions. Th is paper proposes and implements a system for activity recognition in the home environment with a set of switch sensors and a practical text - based sampling tool.