Measure Human Attention and Engagement

Analyzing facial expressions, emotions, and attention during video calls in a caring and more human way.

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Using only the end-users camera. All data remains in the browser. 

How we detect emotional pleasantness and engagement

Immersively.care integrates state-of-the-art deep learning models, which were trained on proprietary annotated images containing real-world information about emotional valence and arousal from individuals from diverse backgrounds in various life situations.

1. Estimation of emotional pleasantness

  • Pleasantness or valence is the affective quality referring to the intrinsic attractiveness of the video call experience, as suggested by the facial expression of attendees.
  • Immersively.care uses a deep learning model to predict the degree of pleasantness on a scale of -100% (unpleasant) to 100% (pleasant) in real-time. 

2. Estimation of emotional engagement

  • Engagement or arousal is defined as the degree of excitement or motivational activation that a participant experiences during a video call.
  • Immersively.care uses a deep learning model to predict the degree of engagement on a scale of -100% (passive) to 100% (actively engaged) in real-time. 

3. Prediction of emotional affect

  •  An emotional affect represents the underlying experience of feeling, emotion or mood.
  • Affects are illustrated using a quadrant of the 2D emotional space that combines how positive or negative the state of mind is (pleasantness), and how passive or active the experience of a video call is (engagement). 
  • When the degree of pleasantness and engagement are known, Immersively.care determines the corresponding top 3 emotional affects for each participant reliably and in real-time.

How we detect attention and interest

Immersively.care estimates the attention level of the user to the screen, as well as the level of interest and sentiment using artificial intelligence.

1. Gase and facial posture detection

Immersively.care integrates deep learning models for head pose tracking, eye gaze direction, eyes closure, and other facial features mapped to the hidden variable of visual attention during a video call. The pre-trained models return a value close to 100% to represent attention, or close to 0% to represent distraction.

2. Interest and sentiment detection

Comprehensive facial features, arousal, valence, emotions, and affects are used to estimate the level of interest and sentiment, as expressed by a participant in relation to what is being said or presented during the video encounter, in real-time. 

What Researchers Are Saying

"Immersively.care's application of facial video-based frameworks to estimate physiological data using Remote Photoplethysmography is a revolution in the field of virtual meetings. I tested it with my Smart Watch on my wrist. Give it a try!"

Mark S.Mark S.Assistant Professor

"I was quite skeptical at the beginning, because of the lack of scientific consensus on the definition of emotions. Immersively.care found a way to ethically offer emotions as an additional language of communication during a video call. Great job!"

Sarah B.Sarah B.Chief Psychologist

"I like how the technology doesn't try to read people's minds but instead seeks to copy the human action of looking at someone's face to guess how they're feeling. This extra context can be helpful during a remote call"

Roseta S.Roseta S.Accessibilty Researcher

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DigIntu Tech Ltd

Hermanova 61

17000 Prague

Czech Republic

EU VAT-ID CZ05233518

Registered in Commercial Register with the Municipal Court in Prague, section C, folder 260406.

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