In today's world, people face more stress than ever before, which can lead to serious health problems.
The 2019 Global Emotions Report by Gallup revealed that around 55% of American adults displayed negative behavior due to stress. This percentage is significantly higher than the worldwide average of 35% for individuals experiencing stress in 143 countries.
Stress can cause anxiety, depression, and digestive issues and may even contribute to life-threatening conditions such as heart attacks and strokes.
Managing stress is essential, and one key factor is effectively monitoring it.
Unconscious movements like teeth grinding, leg tapping, and hand rubbing can be potential indicators of stress.
These unintentional micro gestures reveal emotional states, even when someone is trying to conceal them.
A recent study published in the journal Advanced Intelligent Systems introduces EmoSense, a promising technology for wearable systems that can accurately detect micro gestures associated with stress.
EmoSense uses a capacitive sensing technique to track micro gestures and insulates against unwanted human contact. The conductivity of the body and resistance of the skin measure changes in alternating current passing through the body due to micro gestures.
In a pilot study, EmoSense was tested with 16 participants, and the researchers found a significant positive correlation between the frequency of micro gestures and stress levels. The study discovered a mathematical model linking micro gestures to stress response, indicating how often micro gestures occur could tell how stressed a person feels.
The study also revealed that even when consciously controlling facial or vocal expressions, individuals experiencing high levels of stress or nervousness exhibited unconscious hand-based micro gestures.
The research team plans to explore the underlying correlations between other negative emotions and micro gestures and is considering how EmoSense could be integrated into wearable electronics.
The three-layer design of the system helps prevent false signals created by mistouching.
The team aims to apply EmoSense to practical applications, design prototypes, and conduct further user studies to assess its effectiveness and usability.
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