Affective Worker Condition Monitoring (formerly Health status monitoring of human workers)
General Description
The Affective Worker Condition Monitoring (AWCM) is a component designed to detect psychophysiological states that influence workers' conditions by leveraging biometric data and contextual information (e.g., ocular activity, cardiovascular signals, task progression, and product iteration).
AWCM adopts a knowledge-driven approach combined with AI algorithms to estimate psychophysiological states—such as stress, visual fatigue, and attention levels—using biometric signals collected from wearable devices.
The main goal of this component is to understand and model workers' psychophysiological status and utilise this information to enhance their working conditions.
| Resource | Link |
|---|---|
| Dataset | Link to dataset |
AWCM Block Diagram
Contact
The following table includes contact information of the main developers in charge of the component:
| Name | Organisation | |
|---|---|---|
| Aitor Toichoa Eyam | aitor.toichoaeyam@tuni.fi |  |
License
Technical Foundations
AWCM has been developed to build personalised models for each worker, enabling the system to account for the individual variability and unique characterisitcs of every user. This approach allows predictions and assessments to be tailored to the specific and contextual profile of each worker.
How to use
This component has been developed using data specific to the defined use cases. Consequently, it is intended exclusively for operation within those context and should not be applied outside the scope of the designated use cases.