Using Deep Learning to Identify Persons by their Movement on a Sensor Floor
Felicia Bader, Laura Liebenow, Axel Steinhage · 2023
We present an approach to identify persons based on their movement on a sensor floor. Three types of deep learning neural networks were trained on five subjects’ sensor data collected during ordinary working days in a test room. A Transformer network architecture proved to be the most successful, achieving a recognition rate of over 90% in the task of assigning just one minute of movement data to the correct person. Since the sensor floor can be installed invisibly under normal flooring, the findings result in new applications, e.g. for security systems or in the early detection of health problems that are reflected in the gait pattern.