Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.