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Found 13 papers, 5 papers with code
A Survey on Physiological Signal Based Emotion Recognition
Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification.
ECG Heartbeat Classification Using Multimodal Fusion
We achieved classification accuracy of 99. 7% and 99. 2% on arrhythmia and MI classification, respectively.
Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion
In this paper, we report our findings on a new study on VR stress assessment, where three stress levels are assessed.
Inertial Sensor Data To Image Encoding For Human Action Recognition
To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework.
ECG Heart-beat Classification Using Multimodal Image Fusion
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal.
CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors
Experiments on three publicly available multimodal HAR datasets demonstrate that the proposed MGAF outperforms the previous state of the art fusion methods for depth-inertial HAR in terms of recognition accuracy while being computationally much more efficient.
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data
The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities.
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors
One of the major reasons for misclassification of multiplex actions during action recognition is the unavailability of complementary features that provide the semantic information about the actions.
Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal
To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction.
Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations.
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
CNNs are trained on input images of each modality to learn low-level, high-level and complex features.
Dendrite suppression of metal electrodeposition with liquid crystalline electrolytes
We study the dynamics of electrodeposition of a metal in contact with a liquid crystalline electrolyte.
Applied Physics Chemical Physics
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth.
