Physiological data are nowadays frequently used to recognize the affective state of subjects while performing different tasks. Automatic recognition of a stressful state as a consequence of a high level of cognitive load is significant to prevent illnesses like depression, anxiety and sleep disorders that are often due to excessive workload. The spread of wearable sensors that are increasingly reliable and comfortable makes them easy to use in day-life activities. However, due to the nature of experiments that involve subjects, the cardinality of the acquired data is often low, making difficult to train deep learning methods from the scratch. In this paper we consider the photopletismography (PPG) that measures the blood volume registered just under the skin, which can be used to obtain the heart rate of the subject. It is well known that PPG data are particularly relevant to detect high level of arousal that is activated by stress. We show that, converting monodimensional photopletismography (PPG) data into bidimensional signals it is possible to apply a pretrained CNN, obtaining deep features that outperform handcrafted ones in classification tasks, especially introducing feature selections strategies to avoid curse of dimensionality.

Gasparini, F., Grossi, A., Bandini, S. (2021). A Deep Learning Approach to Recognize Cognitive Load using PPG Signals. In ACM International Conference Proceeding Series (pp.489-495). Association for Computing Machinery [10.1145/3453892.3461625].

A Deep Learning Approach to Recognize Cognitive Load using PPG Signals

Gasparini F.
Primo
;
Bandini S.
Ultimo
2021

Abstract

Physiological data are nowadays frequently used to recognize the affective state of subjects while performing different tasks. Automatic recognition of a stressful state as a consequence of a high level of cognitive load is significant to prevent illnesses like depression, anxiety and sleep disorders that are often due to excessive workload. The spread of wearable sensors that are increasingly reliable and comfortable makes them easy to use in day-life activities. However, due to the nature of experiments that involve subjects, the cardinality of the acquired data is often low, making difficult to train deep learning methods from the scratch. In this paper we consider the photopletismography (PPG) that measures the blood volume registered just under the skin, which can be used to obtain the heart rate of the subject. It is well known that PPG data are particularly relevant to detect high level of arousal that is activated by stress. We show that, converting monodimensional photopletismography (PPG) data into bidimensional signals it is possible to apply a pretrained CNN, obtaining deep features that outperform handcrafted ones in classification tasks, especially introducing feature selections strategies to avoid curse of dimensionality.
slide + paper
CNN; cognitive load; deep features; handcrafted features; Photopletysmography;
English
14th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2021 - 29 June 2021 through 1 July 2021
2021
ACM International Conference Proceeding Series
9781450387927
2021
489
495
reserved
Gasparini, F., Grossi, A., Bandini, S. (2021). A Deep Learning Approach to Recognize Cognitive Load using PPG Signals. In ACM International Conference Proceeding Series (pp.489-495). Association for Computing Machinery [10.1145/3453892.3461625].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/327626
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