Real-time pilot experiments
REGRESSION
The pilot study experiments described in this section were only evaluated on one subject. Although the results obtained from these target achievement tests are therefore not representative, they may give insights on how different means and adaptions in the used algorithms can affect the achieved online success rates in gesture recognition with kNN (with For each pilot experiment, the user first trained the system by capturing data from the exertion of the full-intensity gestures. Each gesture had to be held for 2 s—as in the offline training, resulting in 400 training samples per gesture and repetition. This time, five training repetitions were gathered, i.e. 2000 8-value sample vectors per gesture (see Table In the prediction phase of each pilot experiment, all gestures (apart from rest) were not only tested on full-intensity exertion, but on three different intensity levels (As a measure of comparison, the accuracy of ridge regression with random Fourier features (RR-RFF) as state-of-the-art gesture recognition method was also evaluated in each test run.Captured training data for online pilot experiments and online user studies with overall number of sample vectors resulting from numbers of participants, repetitions and 2 s capturing at 200 Hz