In today's environment, stress is widely recognised as a serious health issue. Stress is thought to be the root cause of a high percentage of industrial-related sick days. While a small amount of stress can be helpful, chronically excessive amounts of stress can be harmful to your health. Stress can cause or exacerbate a number of health conditions, including depression, panic attacks, hypertension, diabetes, and cardiovascular disease. Affective Computing is a growing topic of study in computer science due to the rising focus on people in modern technologies. Affective computing has demonstrated promising results in recognising human stress, according to existing studies. Numerous methods have been used to detect stress including heart rate variability (HRV), skin conductance, pupil diameter, Finger Temperature, and so on. The following are some of the most commonly used methods: Affective Computing's key stroke dynamics, a widely available yet underutilised resource, lies at the heart of this investigation (KSD). Current KSD-based affective computing and biometrics research demonstrates the importance of key stroke variations as a significant data source for determining psychological and emotional states. Keystroke variances can be used to determine stress levels, according to our methods. Application-targeted Based on a person's typical typing habits, an individual's unique keystroke pattern profile is generated. For a set of typing features, this profile has trained average values. In order to determine an individual's stress level, real-time stress-specific variations of these traits are analysed.

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