Smart devices technology is continually advancing and growing, attracting developers to new trends of applications such as navigation and localization applications. Meanwhile, human physical activity recognition using Micro Electro-Mechanical Sensors (MEMS) has been extensively applied for different fields such as health monitoring, emergency services, athletic training, sport rehabilitation, and elderly assistance. Since most smartphones nowadays are equipped with motion sensors, this allows for an opportunity to use them for navigation applications to provide knowledge about a person motion and activity. Specifically, indoor navigation systems would benefit from the knowledge about user motion and dynamics. In this paper, an optimized and adaptive technique is proposed for determining the device use case to be utilized by a navigation system to deal with different time-varying device locations and usages. The proposed algorithm employed different statistical parameters based on accelerometers' and gyroscopes' measurements and quantities derived therefrom to estimate the device's use case. Different test scenarios were conducted to assess the performance of the proposed technique. The results concluded that the proposed technique is able to recognize the correct device use case for each test.