Using Probability Density Function in the Procedure for Recognition of the Type of Physical Exercise
Abstract
This paper presents a method for recognition of physical exercises, using only a triaxial accelerometer of a smartphone. The smartphone itself is free to move inside subject's pocket. Exercises for leg muscle strengthening from subject's standing position squat, right knee rise and lunge with right leg were analyzed. All exercises were performed with accelerometric sensor of the smartphone in the pocket next to the leg used for exercises. In order to test the possibilities of the proposed recognition method, the exercise opposite knee rise with the same position of the sensor was randomly selected. Filtering of the raw accelerometric signals was carried out using Butterworth tenth-order low-pass filter. The filtered signals from each of the three axes were described using three signal descriptors. After the descriptors were calculated, for each of the descriptors a probability density function was constructed. The program that implemented the proposed recognition method was executed online within an Android application of the smartphone. Signals from two male and two female subjects were considered to make a reference for exercise recognition. The exercise recognition accuracy was 94.22% for three performed exercises, and 85.33% for all four considered exercises.
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