Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different Machine Learning Approaches

In this work, the classification of walking direction based on ultrasonic signals has been examined for entrance counting. Feed-forward and recurrent neural network architectures as well as simpler machine learning techniques have been investigated and compared with classical signal processing techniques.Using only a single ultrasonic receiver, the focus was set on the development of a hardware-efficient system concept. Different ultrasonic measurement methods in time and frequency domain have been compared with the perspective of a holistic energy optimization. The analysis of the system’s hardware efficiency was completed by an estimation of algorithmic latency, energy and storage consumption based on the arithmetic of the classification algorithms. All algorithms showed an estimated energy consumption of less than 10 μJ for a single inference on a state-of-the-art implementation of an ARM® Cortex® M4F micro-controller, which was found to be negligible compared to the energy of the measurement principle. Compared to other sensor types and multi-sensor systems, a state-of-the-art test accuracy of 99.72% could be achieved for differentiating between the two entrance directions of a present person and the absence of a person.