The Unfolding Future of Emotion Recognition
Diving into the Untapped Depths of Emotion-Sensing Technology
Welcome to the latest edition of Trend Hacker! It’s a pleasure to have you along as we journey into the intriguing “forces of the future.”
In this edition, the spotlight is on a trend with immense potential to reshape numerous products and services. However, before we deep dive, please consider this “word of notice”:
The impact analysis presented here strives to be pragmatic, considering challenges from operational development to social acceptance. However, given the inherent nature of such explorations, it’s speculative.
Lastly, while the analysis explores how this trend can influence the “fabric of our lived reality,” it does so by pre-selecting some contributors from a pool of over 300. Therefore, consider this analysis a thought-provoking, speculative, selected snapshot of what may lie ahead. Applying the trend to your business might require further contextualization and deep diving into the logic and product.
What is the trend of this Trend Hacker Edition?
Automated Emotion Recognition (AER)
I am especially fond of this topic, as I had the pleasure of working on developing a wearable in this direction. Yet what does it mean: Automated Emotion Recognition, or short (AER), implies using technology to measure, analyze and categorize emotions.
What is part of this analysis, and what is not?
This Trend Hacker edition focuses on individual AER. It doesn’t explore broad data mining, group emotion analysis, or extensive data mining as currently applied for the stock market, banking, sentiment, or similar. I also will not focus much on the 2nd wave of AER, which would include 24/7 “emotional” tracking.
Let’s set the context
AER can be traced back to its root with the pioneering work of psychologist Paul Ekman in the 1960s and 1970s. Ekman’s research identified six basic emotions - happiness, sadness, fear, surprise, anger, and disgust - that he believed were universally experienced in all human cultures. In the 1990s, Dr. Rosalind Picard “automated” emotional sensing enabling computers to recognize, interpret, process, and simulate human emotions. Her work established the Affective Computing Research Group at MIT Media Lab. From there, several researchers from psychology, computer science, neuroscience, and many other disciplines expanded signal indicators to understand people’s emotional states.
How can we measure “emotions”?
Such measurement is possible by biological and behavioral indicators, psychological signals, or mixed with contextual analysis.
Under the biological indicators, we see the growing field of facial expressions, posture movements, gait, and body gestures that, in particular circumstances, can highlight the change in an emotional state.
Behavioral indicators are speech and text, and it is a group in which we might see an explosive growth of use cases through ChatGPT and similar models.
Finally, psychological signals capture the intimate emotional state by measuring, for example, EEG, ECG, EMG, EDA, respiration rate, temperature, etc.
Hacking the trend!
“Hacking” in the context of innovation and trend analysis usually refers to finding novel or creative ways to use, approach, or apply a technology or trend. Further, it is about understanding and manipulating the system to achieve a specific goal. At Trend Hacker, I can support you in the first part of the “hacking “process. The “manipulation,“ or in corporate terms, contextualization for your specific goal, underlies each organizational context.
Let’s start the “hacking“!
Affective Emotion Recognition holds substantial potential, projected to generate a revenue of 60 billion USD by 2023, expanding at a 13% CAGR (Bloomberg, Verified Market Research, 2023). Propelled by escalating data volume, there is an increased need for filters to refine information and create personalized solutions. To illustrate, the average global IP download traffic per individual amounts to 0.63 gigabytes daily (Cisco, 2020).
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