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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Wearable-based human flow experience recognition enhanced by transfer learning methods using emotion data.

Muhammad Tausif Irshad¹, Frédéric Li¹, Muhammad Adeel Nisar²

¹Institute of Medical Informatics, University of Lübeck, Germany.

Computers in Biology and Medicine

|September 28, 2023

Summary

This summary is machine-generated.

Physiological sensors can detect flow states, enhancing work productivity. Combining multiple sensors and using emotion recognition improved flow state detection accuracy, showing a link between emotions and flow.

Keywords:

Artificial neural network Deep learning Flow Human flow experience Machine learning Multimodal sensing Physiological responses Transfer learning Wearable sensors

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Area of Science:

Human-computer interaction
Affective computing
Cognitive science

Background:

Flow experience is a positive affective state of complete absorption, linked to enhanced performance and well-being.
Objective measurement of flow using wearable physiological sensors is underexplored, with most research relying on self-reported data.
Understanding flow states can optimize work environments and individual productivity.

Purpose of the Study:

To automatically discriminate between flow and non-flow states using physiological data from wearable sensors.
To identify the most effective wearable sensor modality for flow detection.
To investigate the relationship between emotions and flow states using transfer learning.

Main Methods:

Collected physiological data from 25 subjects during arithmetic and reading tasks using Empatica E4, Emotiv Epoc X EEG, and Biosignalplux RespiBAN sensors.
Employed feature engineering and deep feature learning for automatic discrimination between flow and non-flow states.
Utilized transfer learning with an emotion recognition task (DEAP dataset) to enhance flow recognition performance.

Main Results:

Electroencephalography (EEG) sensors showed the best individual performance, achieving 64.97% accuracy and 64.95% AF1 score.
Fusing data from all sensor modalities significantly improved performance to 73.63% accuracy and 72.70% AF1 score.
The transfer learning approach using emotion recognition boosted performance to 75.10% accuracy and 74.92% AF1 score.

Conclusions:

Multimodal physiological sensor data effectively discriminates between flow and non-flow states.
Emotions are connected to flow states, and emotion recognition can serve as a latent task to improve flow detection.
Wearable sensor technology offers a promising avenue for objective flow experience assessment.