Thesis - Instituto de Telecomunicações

4.1. HUMAN-COMPUTER INTERACTION 674.1.2 Feature ExtractionEach stroke is characterized by a feature vector, f, which contains relevant information forthe recognition system.Feature extraction is based on the spatial and temporal vectors described previously.The vectors px ∗ , py ∗ , θ, c, c ′ , v x , v y , v, a, a ′ , and w are statistically analyzed and 5 values arecomputed per stroke: the minimum, maximum, mean, standard deviation, and (maximum- minimum). This analysis produces the first 55 features, f 1 ...f 55 . Features f 56 = t n andf 57 = s n−1 correspond, respectively, to the duration and the length of the stroke.Two other features regarding the stroke path are the following. Straightness, (f 58 ), isdefined as the ratio of the Euclidean distance between the starting and ending points of thestroke, and the total path distance:straightness =√(px1 − px n ) 2 +(py 1 − py n ) 2s n−1. (4.8)Jitter (f 59 ) is related to the tremor in the user movements, and it is calculated by analyzingthe ratio between the original path length and the smoothed path length:jitter = s′ n ′s n−1. (4.9)The curvature vector is processed searching for high curvature points, that we call criticalpoints. The process of finding the number of critical points (f 60 ) is the following:⎧⎨ 1 if ∆c i =0∧|c i | >αz i =(4.10)⎩ 0 otherwisen∑n critical points = z i . (4.11)We search for zeros in c ′ , and select the points that have absolute curvature higher thanrada constant α = π 10.pixel 2We compute the time to click (f 61 ) as:i=1time to click = t n − t n−1 ; (4.12)