Abstract
We discuss the merits of adaptive statistical models for biosignals in a daily life context. Processing of this type of signals poses a number of challenges. First, it is clear that an adaptive model is needed to tailor for the differences in physiology between individuals, as well as adapt to someone's current physiological state. Second, in a daily life setting we use unobtrusive measurement devices, which will lead to reduced signal quality compared to the laboratory setting. Third, low-power portable sensors allow for only limited data storage and data transmission. Two techniques to address these challenges are discussed in detail: the usage of the cumulative histogram and parametric models. We show applications to electroencephalogram (EEG), electrocardiogram (ECG) and skin conductance (SC) signals and we advise on how to obtain the most reliable results.