ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

• URL: http://arxiv.org/abs/2104.14278v1
• Date: Thu, 29 Apr 2021 11:53:01 GMT
• Title: ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals
• Authors: Arman Iranfar, Adriana Arza, and David Atienza
• Abstract summary: We propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. Our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.
• Score: 5.042598205771715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.

Related papers

• An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification [2.2940141855172036]
  In molecular biology, there has been an explosion of data generated from multi-omics sequencing. Traditional statistical methods face challenging tasks when dealing with such high dimensional data. This study, focused on tackling these challenges in a neural network that incorporates autoencoder to extract latent space of the features.
  arXiv  Detail & Related papers  (2024-05-16T01:45:55Z)
• AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification [0.0]
  This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband radar signals. Experiments demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-02-27T11:42:26Z)
• Representation Learning for Wearable-Based Applications in the Case of Missing Data [20.37256375888501]
  multimodal sensor data in real-world environments is still challenging due to low data quality and limited data annotations. We investigate representation learning for imputing missing wearable data and compare it with state-of-the-art statistical approaches. Our study provides insights for the design and development of masking-based self-supervised learning tasks.
  arXiv  Detail & Related papers  (2024-01-08T08:21:37Z)
• Active Foundational Models for Fault Diagnosis of Electrical Motors [0.5999777817331317]
  Fault detection and diagnosis of electrical motors is of utmost importance in ensuring the safe and reliable operation of industrial systems. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples. We propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples.
  arXiv  Detail & Related papers  (2023-11-27T03:25:12Z)
• How adversarial attacks can disrupt seemingly stable accurate classifiers [80.2657717174889]
  Adversarial attacks dramatically change the output of an otherwise accurate learning system using a seemingly inconsequential modification to a piece of input data. Here, we show that this may be seen as a fundamental feature of classifiers working with high dimensional input data. We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability.
  arXiv  Detail & Related papers  (2023-09-07T12:02:00Z)
• Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions [80.88681952022479]
  We introduce a new method for combining semi-supervised with multiple-instance learning. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains in per-subject tremor detection.
  arXiv  Detail & Related papers  (2023-04-29T12:25:10Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Bayesian Active Learning for Wearable Stress and Affect Detection [0.7106986689736827]
  Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasive computing. In this paper, we propose a framework with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks. Our proposed framework achieves a considerable efficiency boost during inference, with a substantially low number of acquired pool points.
  arXiv  Detail & Related papers  (2020-12-04T16:19:37Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.