Inspiration
Burnout develops gradually over time as a result of inadequate sleep, emotional stress, and ongoing stress. However, the majority of wellness tools either respond too slowly or provide general guidance without providing an explanation. Using explainable analytics rather than intrusive tracking, I wanted to investigate whether data and AI may reveal early signs of cognitive overload prior to burnout.
What I Constructed
An AI-powered software for cognitive wellbeing called Cognisense uses publicly available data on stress/burnout, emotional tone, and sleep deprivation to predict cognitive load. The system emphasizes on early risk awareness, assisting users in understanding what is exhausting them and what to do next, rather than diagnosing mental health disorders. I used Hex to transform analytics into *a phone-like, real-time experience that: *detects an increase in mental strain *uses AI to explain the cause *offers straightforward, doable solutions
How it Works
Sleep, emotion, and burnout are linked at the population level through the utilization of publicly available datasets. An interpretable Cognitive Load Index (CLI) is created by combining these signals: CLI=0.4⋅Sleep Risk+0.35⋅Emotional Load+0.25⋅Stress Baseline The Hex Data App simulates real-time inputs to show how cognitive load varies dynamically and why alerts emerge when they do.
Why it Matters
Cognisense demonstrates how analytics can become proactive, conversational, and human-centered by going beyond dashboards. It illustrates a future in which data not only reports burnout but also aids in its prevention.
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