3 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
Inertial Sensor Data To Image Encoding For Human Action Recognition
Zeeshan Ahmad, Naimul Khan
Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HA…
CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors
Zeeshan Ahmad, Naimul khan
Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. However, extracting and fusing intermediate features from dif…
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors
Zeeshan Ahmad, Naimul Khan
One of the major reasons for misclassification of multiplex actions during action recognition is the unavailability of complementary features that provide the semantic information…
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data
Zeeshan Ahmad, Naimul Khan
This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Fr…
Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal
Zeeshan Ahmad, Naimul Khan
Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing litera…