Text by: Vincenzo Ronca – Bioengineering Researcher – BrainSigns; Gianluca Borghini – BrainSigns
One of the central actors of the WorkingAge Of Wellbeing (WAOW) tool is the Decision Support System (DSS): such a system is a set of machine learning algorithms that support the worker while dealing with working activities. In particular, the DSS combines the use of models and analytic techniques with traditional data access and retrieval functions to assess the worker’s cognitive and emotional states and provide recommendations accordingly. The DSS which will be employed within the WorkingAge project can be defined as a knowledge-based systems, which implies the interventions of the system based on a-priori established criteria. One of the most relevant entities of the WAOW tool interacting with the DSS is the Ontology model. In this regard, the Ontology will be the criteria to describe the worker state: within the WA project, the Ontology will be an accurate and specific technical database with the aim of describing all the possible user’s physical, cognitive and emotional states. In other words, the Ontology will provide the technical terminology to describe the user state, the DSS will provide actions or interventions accordingly. The ontology will interact with the DSS according to the Stimulus – Organism – Response model. The DSS structure will be based on decidable subset of First-Order Logic, extended to support probabilistic reasoning. Such a system will be technically developed through a logic programming language: the chosen platform is Problog, a probabilistic logic program derived by Prolog. Such a platform is a logic program in which some of the facts are annotated with probabilities.
The DSS will consider different factors that may affect the wellbeing of the worker:
- Environment (e.g., loudly, incorrect lighting).
- Wrong behaviour at work (e.g., use of wrong tools or used in a wrong way, posture).
- User habits and state (including out-of-work elements such as food, sleep).
The final aim of the DSS consists in improving quality of work and life of users by analysing the various factors and providing ad-hoc suggestions timely, for instance:
- “You look tired, it’s time to take a pause.”
- “The working area is getting noisy… it’s safer to use earmuffs, don’t wait!”