Evaluation of prototype reduction algorithms
In order to evaluate the performance of the chosen prototype reduction algorithms (see Sect. These were pilot results to test the algorithms’ accuracy and processing times with reduced datasets.The reduction was executed on each cross-validation fold of the dataset individually, followed by the actual validation. As the considered algorithms comprise kNN-calculations inside, specifically for obtaining the validation accuracy, its parameters had to be defined. Following the recommendations in Sect. Average reduction times for seven gestures (rs, pw, pn, fl, ex, pr, su)The detailed examinations and results for varying datasets were presented in previous work [With respect to reduction time, MGauss, Chen and LVQPRU exposed a broad variance, leading to the presumption of reduced time determinism. Furthermore, these showed the highest means and medians of runtime, so that MGauss, LVQPRU and Chen were disregarded, too.With that, LVQ3 and DSM (also based on LVQ) were the techniques to be chosen for a real-time implementation. With a low runtime of about 0.2 ms in most cases and a low time variance [In order to deeper analyze DSM’s suitability for embedded systems, an assessment of the runtime complexity will be made in the subsequent section.