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The Emerging Power of AI for Attention Measurement

The Emerging Power of AI for Attention Measurement

As attention becomes a valuable currency, businesses and organizations turn to innovative technologies to understand and optimize strategies and performances.

In our Glossary of Attention, we discussed the tools and software enabling professionals to measure their audience attention. Now, we would like to dive deeper into them, making them easier to understand.

There are traditional ways such as EEG, MRI, and MEG which require hard skills in neuroscience, bio-engineering, and medicine to understand the outputs and to make them work. Of course, the results are very accurate.

Different from, there are new ways to measure attention and they do not require specific or technical skills. The new techniques provide a more dynamic way to obtain great insights to understand better your customers’ needs and desires.

A brief overview of the tools and techniques

  • SaaS for facial microexpressions: Microexpressions are expressions occurring within a fraction of a second. This involuntary emotional leakage reveals people’s true emotions. In real-time, these expressed emotional states are detected by computer algorithms that record facial expressions via webcam. Since it gained popularity in several applications, from marketing research to health care, facial expression analysis has been a popular research area in Computer Vision. We can now analyze facial expressions more efficiently and accurately with software that uses machine learning models to read micro-expressions rapidly and automatically. EmPower, for instance, can measure people’s attentional and emotional responses quickly and easily. 
  • Gaze Detection: This technique is to locate the position on a monitor screen where a user is looking. To perform eye tracking, image recognition with deep learning is used. People use gaze tracking to determine their focus of attention. There are many different domains where eye tracking is becoming an increasingly important capability, including security, psychology, computer vision, medical diagnosis, and marketing. 
  • Gaze Estimation Prediction involves using machine learning algorithms to guess where a person is likely to look due to the large amount of data used to train the algorithm.  This type of software uses saliency maps to obtain the final output, so it relies on the difference in contrast of the elements within the image, not considering the movements of the micro-saccades, which are crucial to determining a correct result.
  • Eye Tracking: The idea is to use a device that can precisely measure the eye gaze which only provides information concerning covert attention.
  • Mouse Tracking: The mouse can be precisely followed while an Internet browser is opened by using a client-side language like JavaScript. The mouse’s precise position on the screen can be captured using homemade code or existing libraries. It could be less reliable than other techniques but it is cheaper.

We hope it was helpful, if you want to explore more you can check EmPower.


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