A person's mental wellness plays a critical role in determining how efficiently a complex task that requires cognitive abilities is successfully completed. However, current solutions do not offer a mechanism for detecting an individual’s mental wellness in a real-time and proactive manner. To address that lack, techniques are presented herein that leverage a range of information including electroencephalography (EEG) signals (e.g., as captured from an EEG headset); real-time health parameters such as heart rate, blood pressure, etc. (e.g., as captured from health monitoring devices such as wearable physical fitness monitors); etc. Such information may be processed by an online communication and collaboration facility at a network edge and may be shared to different trusted business applications. Further aspects of the presented techniques may encompass a model that comprises artificial intelligence (AI) and machine learning (ML). Such a model may incorporate different parameters, may include a training mode, and may be used to predict the mental state of an individual. By employing the presented techniques, proactive mental health insight data may be fed into a work routing system’s mechanism so that the assignment of new or existing work that requires a higher level of focus (such as handling a Priority 1 or a Priority 2 technical support case, performing financial transactions, providing virtual medical consultation, etc.) may depend upon a person's mental readiness rather than just their physical availability.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.