• Heart rate
• Sleep
• Stigmergic
• Stigmergic receptive field

Dayly monitoring and analisys of is becoming more and more a popular and the detection of deviations of behavioural patterns is a crucial element if we want to assess the quality of daily living without disorders.
The aim of this thesis is to present a sleep monitoring method based on the collection of heart rate data during sleep and a stigmergic analysis of the latter.
In this regard, we present an adaptive anomaly detection method based on multi-layered stigmergic fed by a contact-free sleep tracker. We exploit heart rate data, gathered via the tracker, in order to identify subject's behavioural pattern over human's habits.
This tipe of sistem allows the monitoring at home of any type of patient, to study the changes in the behavioural habits during sleep and detect anomalies. The detection of this variation allows the family or a doctor to become aware of behavioural shifting from the sleep normality of the subject and enable them to verify if this is related to the onset of disorders or diseases that could compromise the stability of the patient's lifestyle.
Exploiting a technique called ballistocardiography the tracker detects the movements of the body, imparted by the ballistic forces (recoil and impact) associated with cardiac contraction and ejection of blood and with the deceleration of blood
ow through the large blood vessels, and produces useful signals, one of which is the heart rate.
Sample data are then processed by via computational stigmergy. Each sample is associated with a deposit of digital pheromone, called mark, defined in a mono-dimensional space and characterized by evaporation over time. Close samples are aggregated into functional structures called trails. A similarity between trails is then computed and a clustering algorithm is applied.
The outcome is a similarity between days of the same subject. At the end an anomaly index ranks the anomaly level of new day or series of days.