Based on foot movement data, we can distinguish between different types of human movement. The thesis presents the process of creating a prototype measurement system, data preprocessing, and testing the performance of recognition during walking and running with various machine learning algorithms. A comparison is shown between the results, when the inputs of algorithm is acceleration or step trajectory.
The manufactured measuring system consists of a Raspberry Pi microcomputer, an inertial sensor, a 3D printed sensor girder and a battery. The sensor is placed on the heel. We measure the acceleration of the foot in the x, y and z direction. The measured data is transferred to a personal computer, where it is processed with a script in the Python programming language. The goal is to eliminate outliers and noise. Then we perform a double integration of the acceleration, which gives the trajectory of the foot. The data of walking and running is appropriately divided into steps. After that, we perform a motion recognition test in the Weka software environment.
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