A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data

• URL: http://arxiv.org/abs/2201.06610v1
• Date: Mon, 17 Jan 2022 19:46:12 GMT
• Title: A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data
• Authors: Maryam Khalid, Emily Willis
• Abstract summary: This paper surveys machine learning methods that deploy smartphone and physiological data to predict emotions in real-time. We showcase the variability of machine learning methods employed to achieve accurate emotion prediction. The performance can be improved in future works by considering the following issues.
• Score: 0.974672460306765
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Emotion prediction is a key emerging research area that focuses on identifying and forecasting the emotional state of a human from multiple modalities. Among other data sources, physiological data can serve as an indicator for emotions with an added advantage that it cannot be masked/tampered by the individual and can be easily collected. This paper surveys multiple machine learning methods that deploy smartphone and physiological data to predict emotions in real-time, using self-reported ecological momentary assessments (EMA) scores as ground-truth. Comparing regression, long short-term memory (LSTM) networks, convolutional neural networks (CNN), reinforcement online learning (ROL), and deep belief networks (DBN), we showcase the variability of machine learning methods employed to achieve accurate emotion prediction. We compare the state-of-the-art methods and highlight that experimental performance is still not very good. The performance can be improved in future works by considering the following issues: improving scalability and generalizability, synchronizing multimodal data, optimizing EMA sampling, integrating adaptability with sequence prediction, collecting unbiased data, and leveraging sophisticated feature engineering techniques.

Related papers

• A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition [1.6328866317851187]
  We introduce a deep neural network that acquires interpretable representations by a hybrid structure of network-temporal encoding and recurrent attention blocks. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset.
  arXiv  Detail & Related papers  (2023-07-06T15:35:14Z)
• A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition [11.928873764689458]
  We conduct a comprehensive evaluation of popular deep learning approaches for emotion recognition. We show that long-range dependencies in the speech signal are critical for emotion recognition. Speed/rate augmentation offers the most robust performance gain across models.
  arXiv  Detail & Related papers  (2022-11-09T17:27:03Z)
• Machine Learning For Classification Of Antithetical Emotional States [1.1602089225841632]
  This works analyses the baseline machine learning classifiers' performance on DEAP dataset. It provides state-of-the-art comparable results leveraging the performance boost due to its deep learning architecture.
  arXiv  Detail & Related papers  (2022-09-06T06:54:33Z)
• 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)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Transformers for prompt-level EMA non-response prediction [62.41658786277712]
  Ecological Momentary Assessments (EMAs) are an important psychological data source for measuring cognitive states, affect, behavior, and environmental factors. Non-response, in which participants fail to respond to EMA prompts, is an endemic problem. The ability to accurately predict non-response could be utilized to improve EMA delivery and develop compliance interventions.
  arXiv  Detail & Related papers  (2021-11-01T18:38:47Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features [2.863100352151122]
  We propose a deep-learning framework denoted as a dynamic entropy-based pattern learning (DEPL) to abstract informative indicators pertaining to the neurophysiological features among multiple individuals. DEPL enhanced the capability of representations generated by a deep convolutional neural network by modelling the interdependencies between the cortical locations of dynamical entropy based features.
  arXiv  Detail & Related papers  (2020-09-26T07:22:07Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.