Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model

• URL: http://arxiv.org/abs/2507.14173v1
• Date: Thu, 10 Jul 2025 16:30:44 GMT
• Title: Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model
• Authors: Karim Alghoul, Hussein Al Osman, Abdulmotaleb El Saddik,
• Abstract summary: This paper introduces a novel hybrid architecture that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Temporal Convolutional Networks (TCNs)<n>Our results show that the proposed solution outperforms the state-of-the-art CNN architecture, as well as a CNN-LSTM model, in emotion recognition tasks with PPG signals.
• Score: 5.791020333796703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human computer interaction has become integral to modern life, driven by advancements in machine learning technologies. Affective computing, in particular, has focused on systems that recognize, interpret, and respond to human emotions, often using wearable devices, which provide continuous data streams of physiological signals. Among various physiological signals, the photoplethysmogram (PPG) has gained prominence due to its ease of acquisition from widely available devices. However, the generalization of PPG-based emotion recognition models across individuals remains an unresolved challenge. This paper introduces a novel hybrid architecture that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Temporal Convolutional Networks (TCNs) to address this issue. The proposed model integrates the strengths of these architectures to improve robustness and generalization. Raw PPG signals are fed into the CNN for feature extraction. These features are processed separately by LSTM and TCN. The outputs from these components are concatenated to generate a final feature representation, which serves as the input for classifying valence and arousal, the primary dimensions of emotion. Experiments using the Photoplethysmogram Dataset for Emotional Analysis (PPGE) demonstrate that the proposed hybrid model achieves better model generalization than standalone CNN and LSTM architectures. Our results show that the proposed solution outperforms the state-of-the-art CNN architecture, as well as a CNN-LSTM model, in emotion recognition tasks with PPG signals. Using metrics such as Area Under the Curve (AUC) and F1 Score, we highlight the model's effectiveness in handling subject variability.

Related papers

• Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention [0.0]
  Brain2Vec is a new deep learning tool that classifies stress states from raw EEG recordings.<n>These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.
  arXiv  Detail & Related papers  (2025-06-12T12:57:19Z)
• SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition [7.291558599547268]
  Human Activity Recognition (HAR) using wearable sensor data has become a central task in mobile computing, healthcare, and human-computer interaction.<n>We propose SETransformer, a hybrid deep neural architecture that combines Transformer-based temporal modeling with channel-wise squeeze-and-excitation (SE) attention and a learnable temporal attention pooling mechanism.<n>We evaluate SETransformer on the WISDM dataset and demonstrate that it significantly outperforms conventional models including LSTM, GRU, BiLSTM, and CNN baselines.
  arXiv  Detail & Related papers  (2025-05-25T23:39:34Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• Instruction-Guided Autoregressive Neural Network Parameter Generation [49.800239140036496]
  We propose IGPG, an autoregressive framework that unifies parameter synthesis across diverse tasks and architectures.<n>By autoregressively generating neural network weights' tokens, IGPG ensures inter-layer coherence and enables efficient adaptation across models and datasets.<n>Experiments on multiple datasets demonstrate that IGPG consolidates diverse pretrained models into a single, flexible generative framework.
  arXiv  Detail & Related papers  (2025-04-02T05:50:19Z)
• Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators [47.371024581669516]
  Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks. Applying SNNs to sequential tasks, such as text classification and time-series forecasting, has been hindered by the challenge of creating an effective and hardware-friendly spike-form positional encoding strategy. We propose a novel PE technique for SNNs, termed CPG-PE. We demonstrate that the commonly used sinusoidal PE is mathematically a specific solution to the membrane potential dynamics of a particular CPG.
  arXiv  Detail & Related papers  (2024-05-23T09:39:12Z)
• RLEEGNet: Integrating Brain-Computer Interfaces with Adaptive AI for Intuitive Responsiveness and High-Accuracy Motor Imagery Classification [0.0]
  We introduce a framework that leverages Reinforcement Learning with Deep Q-Networks (DQN) for classification tasks. We present a preprocessing technique for multiclass motor imagery (MI) classification in a One-Versus-The-Rest (OVR) manner. The integration of DQN with a 1D-CNN-LSTM architecture optimize the decision-making process in real-time.
  arXiv  Detail & Related papers  (2024-02-09T02:03:13Z)
• DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor [2.9175555050594975]
  We present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs) The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.
  arXiv  Detail & Related papers  (2023-10-01T03:48:16Z)
• 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 Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Video-based Facial Expression Recognition using Graph Convolutional Networks [57.980827038988735]
  We introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based facial expression recognition. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0.
  arXiv  Detail & Related papers  (2020-10-26T07:31:51Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

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.