Dynamic activity classification based on automatic adaptation of postural orientation

We propose a dynamic activity classification system with tri-axial accelerometer sensor using adaptation of user´s postural orientation. In general, the sensor module is worn at a fixed position such as waist, head, wrist, thigh, and so on. However, in reality, the tilt of the attached sensor could be changed from time to time in actions such as sitting down, standing up, lying, walking or running. Moreover, most of the users want to wear the sensor at their own favorite positions instead of a recommended position. In these cases, the activity detection methods based on fixed tilt value may produce serious problem in their performance. Therefore, we propose a user adapted activity classification method which enables users to freely wear the sensor everywhere on their torso. In order to decide tilt values corresponding user´s postural orientation, we focused on tilt-free activities such as walking and running. While walking, the algorithm tries to modify the predefined reference tilt values for the three axes, X, Y and Z. From an experiment, we have achieved 88% of the activity classification accuracy even though the tilt angle is changed while wearing sensors.