The CAM-SPM system (Connectionist Association Module Symbolic Processing Part) is a hybrid intelligence architecture that has been designed mainly for modelling physical phenomena for which a priori knowledge is available, either in the form of linguistic rules or in the form of known symbol values. In the framework of ORESTEIA project this architecture has been considered as the most appropriate to implement the state mapping in the biometric artefact.
The generic CAM-SPM architecture consists of a connectionist (subsymbolic) association part, with a Numerical Data component, and a symbolic processing part, with a Semantic Knowledge component. In this modular architecture the Connectionist Association Module (CAM) provides the ability of grounding the symbolic predicates (associating them with the input features), while the Symbolic Processing Module (SPM) implements a semantically rich reasoning process. The Semantic Knowledge module corresponds: (a) to linguistic rules that describe a particular physical phenomenon, and/or (b) to rule databases that may adapted by researchers as more rules describing the new particular physical phenomenon made available. The semantic knowledge component may not exist at all. The Numerical Data module provides the means for (a) the initial training of CAM module, (b) the adaptation of the CAM module to specific contexts.
In the case of the biometric artefact features derived from the heart rate, respiration rate, ECG signal, systolic and diastolic blood pressure are fed to the CAM. The symbolic predicates (intermediate representation) correspond to semantic evaluations of users heart rate, blood pressure as well as of its ECG-pulses form that should be able to provide some indication about the health status of its user, which may fall into four categories: normal, slightly abnormal, abnormal, dangerous. The rules connecting the symbolic predicates with the World Representation are inserted in the system through the SPM module.
Aspects of the CAM-SPM architecture have already been presented in several international scientific papers. A prototype has been demonstrated in suitable events of the Disappearing Computer community. The potential for further use is very high, especially in further research and development efforts. The current state of affairs does not allow immediate commercial exploitation. However component technologies of this architecture can reach this stage in 2-4 years of further developments. We are primarily interested for applications in the areas of Ambient Intelligence, Health Monitoring, and Car Hazard Avoidance.
The current results are expected to be further developed for other applications. Of particular interest is the area of safe driving and health-monitoring of aged, very young or chronically ill people.