Artificial Intelligence at the Digital Workplace – Physiolytics for Flow-aware Notifications
Digital technologies enabling the delivery of information in the form of real-time notifications are the foundation of today’s digital workplace. Beside all its potential, digital technologies also lead to interruptions at work. Thus, designing human-centric intelligent digital notifications is becoming an important challenge.
Flow, a state in which individual employees are completely absorbed and highly concentrated when performing a task was first investigated by psychologist Mihaly Csikszentmihalyi and it has been shown that flow states can in turn lead to a higher level of well-being, satisfaction or performance of the employee. To date, however, researchers have relied primarily on self-reported scales when measuring flow. Recent work in the disciplines of Psychology, Computer Science and Information Systems has focused on the unambiguous identification of flow states using physiological indicators. Physiolytics leverages physiological data and applies machine learning techniques in order to recognize affective-cognitive human states. This endavor also opens up new possibilities for the objective and continuous measurement of flow states in real time.
We introduce an exemplary use case which uses physiological data about a possible flow state of the employee to design human-centric intelligent notifications. Thus, one can decide whether a notification should be delivered immediately (case 1 - employee not in flow) or delayed (case 2 - employee in flow). As a result, the flow state of the employee would not be disturbed and could be maintained. Being able to recognize flow automatically opens up many more use cases. However, beside solving the technological challenges further attention must be also paid to ethical and data protection issues.