Transfer Learning for Subject-Independent Stress Detection using Physiological Signals

Radhika, K; Murthy, O. V. Ramana

doi:10.1109/indicon49873.2020.9342505

Cited by 11 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous works demonstrate that the most of the work is done on time-frequency domain [37]- [39], [47], [49]- [51], [53]- [57], subject-dependent [38], [47], [49], [50], [53]- [56], [58] and using machine learning models [38], [47], [49], [50], [53], [54], [57], [58]. Few researchers used traditional deep learning techniques [36], [48], [51], [55], [56]. Except for the CLAS dataset, the proposed frequency band method outperforms all the subject-dependent and subjectindependent state-of-the-art (SOA) studies that have been presented.…”

Section: F Comparison With Existing Workmentioning

confidence: 99%

Hierarchical Autoencoder Frequency Features for Stress Detection

Kuttala,

Subramanian,

Oruganti

2023

IEEE Access

View full text Add to dashboard Cite

Stress has a significant negative impact on people, which has made it a primary social concern. Early stress detection is essential for effective stress management. This study proposes a Deep Learning (DL) method for effective stress detection using multimodal physiological signals -Electrocardiogram (ECG) and Electrodermal activity (EDA) . The extensive latent feature representation of DL models has yet to be fully explored. Hence, this paper proposes a hierarchical autoencoder feature fusion on the frequency domain. The latent representations from different layers of AutoEncoders(AE) are combined and given as input to the classifier -Convolutional Recurrent Neural Network with Squeeze and Excitation (CRNN-SE) model. A two-set performance comparison is performed (i) performance on frequency band features, and raw data are compared. (ii) autoencoders trained on three cost functions -Mean Squared Error (MSE), Kullback-Leibler (KL) divergence, and Cosine similarity performance are compared on frequency band features and raw data. To verify the generalizability of our approach, we tested it on four benchmark datasets-WAUC, CLAS, MAUS and ASCERTAIN. Results show that frequency band features showed better results than raw data by 4-8%, respectively. MSE loss produced better results than other losses for both frequency band features and raw data by 3-7%, respectively. The proposed approach considerably outperforms existing stress detection models that are subject-independent by 1-2%, respectively.

show abstract

Section: F Comparison With Existing Workmentioning

confidence: 99%

Hierarchical Autoencoder Frequency Features for Stress Detection

Kuttala,

Subramanian,

Oruganti

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Recently, studies have demonstrated possibilities and potential advantages of applying learning to timeseries of physiological time-series (Li et al 2020a, 2020b, Radhika and V Ramana 2020, Bizzego et al 2021. With more advanced models, the mechanisms behind their decision-making become increasingly difficult to understand.…”

Section: Machine Learningmentioning

confidence: 99%

Current trends and opportunities in the methodology of electrodermal activity measurement

Tronstad¹,

Amini²,

Bach³

et al. 2022

Physiol. Meas.

View full text Add to dashboard Cite

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

show abstract

“…Yu and Sano [3] applied semi-supervised learning on leveraging unlabeled data to estimate wearablebased momentary stress. Radhika et al [4], [5] proposed the frameworks that investigate the effectiveness of transfer This work is supported by NSF #2047296 and #1840167.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have explored improving the performance of generic models by introducing person-specific information. For example, Radhika et al [4], [5] used person-specific information in the testing set during the feature extraction and selection. Wu et al [17] achieved model personalization from a pre-trained generic model by active learning approach and improved detection precision on each new patient.…”

Section: Introductionmentioning

confidence: 99%

PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss

Cao¹,

Han²,

Hu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications. However, generic models are usually preferable in real-world applications, due to the difficulties of developing person-specific models, such as new-user-adaptation issues and system complexities. To improve the performance of generic models, we propose a Participant-invariant Representation Learning (PiRL) framework, which utilizes maximum mean discrepancy (MMD) loss and domain-adversarial training to encourage the model to learn participant-invariant representations. Further, to avoid trivial solutions in the learned representations, a triplet loss based constraint is used for the model to learn the label-distinguishable embeddings. The proposed framework is evaluated on two public datasets (CLAS and Apnea-ECG), and significant performance improvements are achieved compared to the baseline models.

show abstract

Transfer Learning for Subject-Independent Stress Detection using Physiological Signals

Cited by 11 publications

References 21 publications

Hierarchical Autoencoder Frequency Features for Stress Detection

Hierarchical Autoencoder Frequency Features for Stress Detection

Current trends and opportunities in the methodology of electrodermal activity measurement

PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss

Contact Info

Product

Resources

About