3 citations · 12 across the 10 of their papers we have counts for
11 papers
A Survey on Physiological Signal Based Emotion Recognition
Zeeshan Ahmad, Naimul Khan
Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion rec…
ECG Heartbeat Classification Using Multimodal Fusion
Zeeshan Ahmad, Anika Tabassum, Ling Guan +1
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine lear…
Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion
Zeeshan Ahmad, Suha Rabbani, Muhammad Rehman Zafar +3
ECG is an attractive option to assess stress in serious Virtual Reality (VR) applications due to its non-invasive nature. However, the existing Machine Learning (ML) models perform…
ECG Heart-beat Classification Using Multimodal Image Fusion
Zeeshan Ahmad, Anika Tabassum, Naimul Khan +1
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…
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…
Dataset of raw and pre-processed speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate measurements
Mohammed Usman, Zeeshan Ahmad, Mohd Wajid
Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oxymeter. Physio…
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
Zeeshan Ahmad, Naimul Khan
Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is per…