Key result
Recent advances in commercially available sensors, signal processing, and deep learning architectures have enabled emotion recognition systems to move from controlled laboratory setups to real-life scenarios.
Why the study?
Bringing emotion recognition out of controlled laboratory setups into everyday life can enable broader applications, but requires advances in sensors and machine learning methods.
This review highlights recent advances and challenges in moving emotion recognition technologies from controlled laboratory settings to real-world applications.
Answers are grounded in this paper and cite their sources.
Emotion sensing may aid real-world cardiovascular monitoring; leaves open prospective validation in clinical cohorts.
Bringing emotion recognition (ER) out of the controlled laboratory setup into everyday life can enable applications targeted at a broader population, e.g., helping people with psychological disorders, assisting kids with autism, monitoring the elderly, and general improvement of well-being. This work reviews progress in sensors and machine learning methods and techniques that have made it possible to move ER from the lab to the field in recent years. In particular, the commercially available sensors collecting physiological data, signal processing techniques, and deep learning architectures used to predict emotions are discussed. A survey on existing systems for recognizing emotions in real-life scenarios—their possibilities, limitations, and identified problems—is also provided. The review is concluded with a debate on what challenges need to be overcome in the domain in the near future.
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Stanisław Saganowski (2022) conducted a review in Emotion recognition. Sensors and machine learning for emotion recognition was evaluated. Recent advances in commercially available sensors, signal processing, and deep learning architectures have enabled emotion recognition systems to move from controlled laboratory setups to real-life scenarios.
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