Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs

• URL: http://arxiv.org/abs/2212.14806v1
• Date: Tue, 20 Dec 2022 12:56:28 GMT
• Title: Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs
• Authors: Mohammad Mahdi Dehshibi and Temitayo Olugbade and Fernando Diaz-de-Maria and Nadia Bianchi-Berthouze and Ana Tajadura-Jim\'enez
• Abstract summary: People with chronic pain unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities. We proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
• Score: 61.080598804629375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.

Related papers

• Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments [67.80453452949303]
  Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments. We propose different model-agnostic learners (so-called meta-learners) to estimate the bounds that can be used in combination with arbitrary machine learning models.
  arXiv  Detail & Related papers  (2024-06-04T16:31:43Z)
• Synthetic Data for Robust Stroke Segmentation [0.0]
  Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data. This paper introduces a novel synthetic data framework tailored for stroke lesion segmentation. Our approach trains models with label maps from healthy and stroke datasets, facilitating segmentation across both normal and pathological tissue.
  arXiv  Detail & Related papers  (2024-04-02T13:42:29Z)
• mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds [8.37609145576126]
  We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN. We demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation. It uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls.
  arXiv  Detail & Related papers  (2023-03-27T08:30:11Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
  We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
  arXiv  Detail & Related papers  (2021-05-18T20:47:45Z)
• Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders [8.518166245293703]
  We propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori.
  arXiv  Detail & Related papers  (2021-01-18T22:53:29Z)
• Multimodal Gait Recognition for Neurodegenerative Diseases [38.06704951209703]
  We propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases. A new correlative memory neural network architecture is designed for extracting temporal features. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
  arXiv  Detail & Related papers  (2021-01-07T10:17:11Z)
• Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets [6.049813979681482]
  We propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific datasets. We show how the expected risk can be decomposed and optimised empirically. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models.
  arXiv  Detail & Related papers  (2020-09-08T22:00:00Z)
• 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)

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.