Performance investigation of machine learning algorithms for simple human gesture recognition employing an ultra low cost radar system

Radar based gesture recognition offers great opportunities to increase user-friendliness of countless applications at home, in transportation and for industries. Here, not only data-intensive image and video processing, but also 1D multi-or single-channel time-series signals are in focus. We examine classical machine learning (ML) approaches and compare them in a reproducible manner. We evaluate the performance of naive methods-such as threshold detection (THD)-and classical ML methods-such as the support vector machine (SVM). The performance is hereby judged by elements such as accuracy, false-positive rate (FPR), training and prediction time, hardware (HW) requirements and real-time capabilities as well as the size of the classifier. To create the library needed for the given investigation, a two channel continuous wave (CW) modulated radar system with carrier frequency of 10 GHz has been employed. We conclude that naive methods are outperformed by all investigated classical ML methodologies. The results in terms of accuracy and FPR are satisfactory. However, there are large differences between naive and ML methods in terms of HW requirements and real time performance. In conclusion, classical ML methods fulfil the defined requirements satisfactorily, only the real-time performance on low-performance HW is limited due to the required computing power. Thus, the algorithms are a good choice for gesture recognition-of 1D multi-or single-channel time-series sianals=.lf applied correctly.