An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals

• URL: http://arxiv.org/abs/2407.15252v1
• Date: Sun, 21 Jul 2024 19:52:44 GMT
• Title: An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals
• Authors: Gelei Xu, Ruiyang Qin, Zhi Zheng, Yiyu Shi,
• Abstract summary: This paper introduces an adaptive framework for personalized stress detection using PPG and EDA signals. Unlike traditional methods that rely on a generalized model, this framework aims to provide each user with a personalized model for higher stress detection accuracy.
• Score: 6.025332045004531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Timely stress detection is crucial for protecting vulnerable groups from long-term detrimental effects by enabling early intervention. Wearable devices, by collecting real-time physiological signals, offer a solution for accurate stress detection accommodating individual differences. This position paper introduces an adaptive framework for personalized stress detection using PPG and EDA signals. Unlike traditional methods that rely on a generalized model, which may suffer performance drops when applied to new users due to domain shifts, this framework aims to provide each user with a personalized model for higher stress detection accuracy. The framework involves three stages: developing a generalized model offline with an initial dataset, adapting the model to the user's unlabeled data, and fine-tuning it with a small set of labeled data obtained through user interaction. This approach not only offers a foundation for mobile applications that provide personalized stress detection and intervention but also has the potential to address a wider range of mental health issues beyond stress detection using physiological signals.

Related papers

• Personalization of Stress Mobile Sensing using Self-Supervised Learning [1.7598252755538808]
  Stress is widely recognized as a major contributor to a variety of health issues. Real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes.
  arXiv  Detail & Related papers  (2023-08-04T22:26:33Z)
• Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification [3.8036939971290007]
  Photoplethysmography signals hold significant potential for continuous fitness-health monitoring. Photoplethysmography signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data.
  arXiv  Detail & Related papers  (2023-07-12T18:05:05Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Sequential Attention Source Identification Based on Feature Representation [88.05527934953311]
  This paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge.
  arXiv  Detail & Related papers  (2023-06-28T03:00:28Z)
• Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings [4.4353357514621745]
  In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time. We show that the context-aware active learning technique we propose achieves a desirable detection performance using 88% and 32% fewer queries from users.
  arXiv  Detail & Related papers  (2023-04-28T22:09:19Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices [9.580380455705397]
  Stress levels can provide valuable data for mental health analytics as well as labels for annotation systems. There is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns.
  arXiv  Detail & Related papers  (2022-03-18T00:36:27Z)
• An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices [7.714433991463217]
  Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods. We introduce a stress-related bio-signal processing pipeline with a simple neural network architecture to improve the performance of subject-independent models. Our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model.
  arXiv  Detail & Related papers  (2022-03-18T00:19:42Z)
• Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation [54.88777449903538]
  We introduce a novel textithybrid automatic differentiation (AD) system for sensitivity analysis. This enables modelling the sensitivity of arbitrary differentiable function compositions, such as the training of neural networks on private data. Our approach can enable the principled reasoning about privacy loss in the setting of data processing.
  arXiv  Detail & Related papers  (2021-07-09T07:19:23Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• 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)

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.