A Deep Learning Approach to Recognize Cognitive Load using PPG Signals

Gasparini, Francesca; Grossi, Alessandra; Bandini, Stefania

doi:10.1145/3453892.3461625

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…For example, Gasparini et al [ 28 ] collected PPG sensor data from 16 adult participants, extracting 20 significant Convolutional Neural Network (CNN) features using the relief feature selection method. They achieved a binary classification accuracy of 79% using hold-out cross-validation.…”

Section: Related Workmentioning

confidence: 99%

Food Choices after Cognitive Load: An Affective Computing Approach

Kappattanavar

Hecker

Moontaha

et al. 2023

Sensors

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Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.

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Section: Related Workmentioning

confidence: 99%

Food Choices after Cognitive Load: An Affective Computing Approach

Kappattanavar

Hecker

Moontaha

et al. 2023

Sensors

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“…Several datasets exist in the literature that acquire multimodal physiological data in the field of emotion recognition, among them DEAP [53], MAHNOB-HCI [54], EMDB [55], AMIGOS [56], ASCERTAIN [57], CASE [58], CLAS [35], and CLAWDAS [32,33]. Three main criteria have been applied for the selection of the two datasets, CLAS and CLAW-DAS, considered here: the presence of cognitive load tasks, the use of wearable devices for physiological signals' acquisition, and the presence of a baseline in the resting state condition.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…To validate the normalization procedure presented here, PPG data belonging to two different datasets were considered: the Cognitive Load and Affective Walkability in Different Age Subjects(CLAWDAS) dataset, partially introduced in [32][33][34], and the Cognitive Load, Affect and Stress recognition (CLAS) dataset, available in [35].…”

Section: Introductionmentioning

confidence: 99%

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Gasparini

Grossi

Giltri

et al. 2022

Signals

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Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.

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“…A bandpass filter between 0 and 4 Hz [26,27] and a down-sampling to 500 Hz were applied to the PPG data [27]. The signals were then divided into 20 s segments [28].…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

Esgalhado

Fernandes²,

Vassilenko

et al. 2021

Computers

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Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

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A Deep Learning Approach to Recognize Cognitive Load using PPG Signals

Cited by 10 publications

References 17 publications

Food Choices after Cognitive Load: An Affective Computing Approach

Food Choices after Cognitive Load: An Affective Computing Approach

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

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