Effective indices to characterize short sequences of human random generations

Brain impediments such as dementia are a serious problem today. It would be very useful if software for private diagnosis were available. In this paper, we show the effectiveness of the human random generation test (HRG) for such software, and propose a set of four indices to be used for classifying the HRG data. Human-generated random numbers have strong characteristics compared to computer-generated random numbers, and these are known to be correlated to the individual characters of the subjects. However, analysis using the correlation dimension or HMM requires a long data sequence, and thus is not suitable for diagnoses.We therefore focus on short sequences of HRG and search for effective indices to detect signs of brain disability hidden in the HRG data. We studied data from subjects of different age groups, and successfully differentiated the data from the different groups.