A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

• URL: http://arxiv.org/abs/2302.01602v2
• Date: Mon, 13 Nov 2023 10:33:32 GMT
• Title: A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate
• Authors: Ashkan Parsi, David O'Callaghan, Joseph Lemley
• Abstract summary: Driver stress is a major cause of car accidents and death worldwide. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.

Related papers

• Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection [5.304745246313982]
  This study explores the generalizability of machine learning models trained on HRV features for binary stress detection. Our findings reveal a crucial factor affecting model generalizability: stressor type. We recommend matching the stressor type when deploying HRV-based stress models in new environments.
  arXiv  Detail & Related papers  (2024-05-06T14:47:48Z)
• Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals [38.497450879376]
  Early detection of hypertension is crucial in preventing significant health issues. Some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram ( PPG) In this paper, a high-dimensional representation technique based on random convolution kernels is proposed for hypertension detection using PPG signals.
  arXiv  Detail & Related papers  (2023-07-31T00:09:23Z)
• Review on Classification Techniques used in Biophysiological Stress Monitoring [0.0]
  Repeated acute stress and continuous chronic stress may play a vital role in inflammation in the circulatory system and thus leads to a heart attack or to a stroke. In this study, we have reviewed commonly used machine learning classification techniques applied to different stress-indicating parameters used in stress monitoring devices.
  arXiv  Detail & Related papers  (2022-10-28T10:23:53Z)
• Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices [36.16114600037677]
  Beam AI SDK can monitor user stress through the selfie camera in real-time. We evaluate our technology on the UBFC dataset, the MMSE-HR dataset, and Beam AI's internal data.
  arXiv  Detail & Related papers  (2022-10-04T17:58:37Z)
• An optimized hybrid solution for IoT based lifestyle disease classification using stress data [2.3909933791900326]
  The proposed novel method employs a test that measures a subject's electrocardiogram (ECG), galvanic skin values (GSV), HRV values, and body movements. The developed approach is capable of dealing with the class imbalance problem by using WESAD (wearable stress and affect dataset) dataset.
  arXiv  Detail & Related papers  (2022-04-04T05:52:48Z)
• Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques [10.09810782568186]
  The methods for measuring human stress responses could include subjective questionnaires and objective markers observed using data from wearable and non-wearable sensors. We explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources.
  arXiv  Detail & Related papers  (2022-02-07T09:48:46Z)
• Personalized Stress Monitoring using Wearable Sensors in Everyday Settings [9.621481727547215]
  We explore objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling.
  arXiv  Detail & Related papers  (2021-07-31T04:15:15Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)
• AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching [76.4844593082362]
  We investigate the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong baseline for remote HR measurement with architecture search (NAS) Comprehensive experiments are performed on three benchmark datasets on both intra-temporal and cross-dataset testing.
  arXiv  Detail & Related papers  (2020-04-26T05:43:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.