Requirements and concept
The experimental studies and the developments which they are based on are driven by the requirements of Sect. First, a pilot dataset of several (full-intensity) gesture exertions is captured from the authors in order to conduct an offline cross-validation analysis of kNN parameters on gesture classification without real-time application.Second, the obtained kNN parameter configuration is applied in a real-time scenario, in which new (full-intensity) gesture data is gathered. Additionally, an approach of proportionality scaling is introduced here. With that, real-time gesture detection performance is measured in an online target achievement test with just one subject. The success rate in this pilot study is utilized to analyze the influence of proportionality scaling parameters while testing three levels of exertion intensity (but just training on full intensity).Third, the two determined parameter configurations (kNN and proportionality scaling) are tested in a real-time user study with 12 subjects (and in an extended user study with 4 subjects). Again, target achievement tests are conducted, including three levels of gesture exertion intensity for detection (but just full-intensity for training). No further parameter optimization takes place in this step. Moreover, a data reduction technique is introduced and applied to each subject’s data. The success-rate performance of the non-reduced and the reduced data approach are compared.