The actigraphy is a cost-effective method for assessing specific sleep disorders such as diagnosing insomnia, circadian rhythm disorders, or excessive sleepiness. Due to recent advances in wireless connectivity and motion activity sensors, the new actigraphy devices allow the non-intrusive and non-stigmatizing monitoring of outpatients for weeks or even months facilitating treatment outcome measure in daily life activities. This possibility has propitiated new studies suggesting the utility of actigraphy to monitor outpatients with mood disorders such as major depression, or patients with dementia. However, the full exploitation of data acquired during the monitoring period requires the use of automatic systems and techniques that allow the reduction of inherent complexity of the data, the extraction of most informative features, and the interpretability and decision-making. In this study we purpose a set of techniques for actigraphy patterns analysis for outpatient monitoring. These techniques include actigraphy signal pre-processing, quantification, nonlinear registration, feature extraction, detection of anomalies, and pattern visualization. In addition, techniques for daily actigraphy signals modelling and simulation are included to facilitate the development and test of new analysis techniques in controlled scenarios.