Md Atiqur Rahman AhadUniversity of East London | UEL
Md Atiqur Rahman Ahad
Ph.D.(KyuTech), M.(UNSW), M.(DU), B.Sc.(Hons)(DU) [SM-IEEE; SM-OPTICA]
Weblink: http://ahadvisionlab.com
About
216
Publications
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Introduction
Personal site: http://ahadVisionLab.com
I am less frequent at RG :)
#Call for Papers:
ABC - https://abc-research.github.io/
IVPR - http://cennser.org/IVPR
ICIEV - http://cennser.org/ICIEV
Journal - http://cennser.org/IJCVSP
FB: https://www.facebook.com/AtiqAhad
atiqahad [AT] gmail.com
Additional affiliations
December 2007 - February 2008
Nikon Corporation, Japan
Description
- Nikon Corporation, Japan
Education
October 2006
March 2002
July 1996
Publications
Publications (216)
Stereoscopic cameras, such as those in mobile phones and various recent intelligent systems, are becoming increasingly common. Multiple variables can impact the stereo video quality, e.g., blur distortion due to camera/object movement. Monocular image/video deblurring is a mature research field, while there is limited research on stereoscopic conte...
Gait recognition is an advanced biometric technology that can be used to identify individuals based on their walking patterns, even from low-spatial-resolution image sequences from security surveillance camera footage. Traditional gait recognition approaches rely on complete body information and often overlook the challenge of occlusion. In real-wo...
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recentl...
Parkinson's disease (PD) patients experience a range of symptoms, necessitating personalized treatment programs. Anti-PD medications are commonplace, yet a scenario can develop in which the Parkinson's medication being taken is no longer as effective as it once was. It may result in the re-emergence of symptoms prior to the next medicine intake. Th...
Personal thermal comfort sensations, which refer to the mental state of feeling comfortable and satisfied with one's surrounding thermal environment, are crucial for overall well-being. This research focuses on measuring physiological indicators such as heart rate, body movement, body temperature, and skin potential using a wearable terminal contai...
Heatstroke is life-threatening, with rising mortality rates attributed to increasing global temperatures. Acute discernment of heatstroke symptoms remains imperative for effective interventions. We proposed an advanced post-processing technique to forecast heatstroke risk, enhancing machine learning algorithms based on physiological features. We im...
The rise in global temperatures has become a significant concern, leading to an increase in heat stroke incidents, which pose severe
health consequences, including mortality. The comfort levels of indoor environments fluctuate depending on various activities
performed in different situations. Physiological data, encompassing heart rate, body temper...
Personal thermal state (PTS) refers to the individual’s thermal condition.
PTS varies from person to person considering both internal and external factors.
Internal factors such as a person’s physical fitness, body weight, Body
Mass Index (BMI), skin color, etc. can impact a person’s responses to any
thermal changes. On the other hand, external fac...
Human Identification at a Distance (HID) is an important research area due to its importance (especially in bio-metrics) and inherent challenges within this domain. To mitigate some of the constraints, we have introduced the HID challenge. This paper presents an overview of the 4th International Competition on Human Identification at a Distance (HI...
In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., dis...
In the previous decade, breakthroughs in central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disable...
Hand gesture recognition is one of the most widely explored areas under the human–computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed...
In our research, we are attempting to predict Autism Spectrum Disorder (ASD) and the associated Autism Diagnostic Observation Schedule (ADOS) scores using data from the body skeleton, head movement, and eye gaze. To the best of our knowledge, no such prior work has been completed. ASD is a neurological and developmental disorder that affects how pe...
Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further...
In this paper, we describe an uncertainty-aware estimation framework for gait relative attributes. We specifically design a two-stream network model that takes a pair of gait videos as input. It then outputs a corresponding pair of Gaussian distributions of gait absolute attribute scores and annotator-dependent gait relative attribute label distrib...
Now-a-days, signal processing is ubiquitous.
This broad electrical engineering discipline is concerned with
extracting, manipulating, and storing information embedded
in complex signals and images. From the early days of the
FFT to today’s machine/computer vision industry, signal
processing has driven many of the products and devices that
have bene...
The paper provides a summary of the Competition on Human Identification at a Distance 2022 (HID 2022), which is the third one in a series of competitions. HID 2022 is for promoting the research in human identification at a distance by providing a benchmark to evaluate different methods. The competition attracted 112 valid registered teams. 71 teams...
Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) se...
Skeleton-based motion capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further...
Japanese packed-meal or Bento preparation or packaging automated by a robot is a recent challenge. It is one of the latest cementations in human activity recognition systems. It shows contingency on physical gesture recognition techniques using some sensor-based datasets, especially some motion capture data or skeleton data. To get a firm grip on t...
Due to the fast advancements of low-cost micro-embedded sensors and MoCap sensors, human action recognition has become an essential study topic and is garnering a lot of interest in different sectors. Recently, it is drawing a lot of attention in human-robot collaboration to assist human to preform regular tasks step-wise because it is difficult to...
The automation system has brought a revolutionary change in our lives. Food packaging activity recognition can add a new dimension to industrial automation systems. However, it is challenging to identify the packaging activities using only skeleton data of the upper body due to the similarities between the activities and subject-dependent results....
This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active...
An indispensable part of life is social interaction. Without any doubt, it can help to overcome Autism Spectrum Disorder (ASD). ASD is defined as an abnormality in social communication, which is not a disease. So, to face this disorder, involvement in social communication is required. It can be possible through therapy sessions. In this modern era,...
A set of advanced approaches and models on human action, activity, gesture, behavior and related aspects are summarized in this note. There are a number of challenges on these domains, and some of these are addressed here. Notably, Video-based human activity recognition, sensor-based activity analysis, skeleton-based activity recognition, assisted...
Human Action Recognition (HAR) is an application-oriented field that utilizes numerous Machine Learning methods to identify diverse human actions or movements to provide an appropriate or suitable response. A HAR method's success largely depends on the performance of the algorithms for data processing and activity prediction working in the backgrou...
Nurse care activity recognition is an emerging segment in healthcare automation systems based on physical movement recognition
applying machine learning techniques using various sensor-based
datasets. In this paper, different machine learning models have
been used to recognize the activities. However, before that, our user
dataset has been preproce...
Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge, the fourth edition of the challenge provides an opportunity to recognize 8 modes of locomotion and transportation (activities) from radio data, including GPS reception, GPS location, WiFi reception, and GSM cell tower scans in a user-independent manner. Though the dataset has prov...
Abstract Background/ Introduction: Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social and cognitive abilities. ASD has no cure so early diagnosis is important for reducing its impact. The current behavioural observation-based subjective-diagnosis systems (e.g., DSM-5 or ICD-10) frequently misdiagnose subjects. There...
Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., em...
A major goal of human activity and behavior recognition (HAR) is to recognize activities and behaviors from a series of action data for different subjects under different environmental conditions. The use of wearables, smart devices, and vision-based systems enables the collection of human action and behavior data for greater health benefits, rehab...
Electrical impedance measurements can detect many diseases and disorders in the human body. Electrical Impedance Tomography (EIT) is a fast-developing medical imaging technique. In this chapter, we present some applications of EIT in lung disease detection. Existing literature in this subject has been investigated, including original research work...
The elderly population of the world is increasing, and along with that, the need for assisted living and health monitoring. Biomedical radars can be used to monitor the health and safety of patients, both at home and in a clinical setup. This chapter gives a comprehensive overview of biomedical radar and antenna systems for contactless human activi...
Contactless sensors have brought a new way of patient monitoring for healthcare applications. The patient is only required to be in the vicinity of the sensor for health monitoring. Also, there is opportunity for a contactless sensing system to monitor multiple patients simultaneously, which is not possible with wearable contact-based sensors. As a...
Skeleton-based Human Action Recognition (SHAR) is one of the most trending research topics in computer vision, which relies on the investigation of multi-modal data acquired from different sensory devices. Due to its faster execution speed, skeleton-based automated SHAR systems are widely adopted in real-time applications such as surveillance syste...
Flawed authentication protocols led to the need for a secured protocol for radio frequency identification (RFID) techniques. In this paper, an authentication protocol named Modified ultralightweight mutual authentication protocol (MUMAP) has been proposed and cryptanalysed by Juel-Weis challenge. The proposed protocol aimed to reduce memory require...
Smartphone sensor-based activity recognition seeks broad, high-level knowledge about human behaviors from multitudes of low-level sensor readings, and makes considerable headway in healthcare domain. Our primary contribution is to study the effective pre-processing technique and the extraction of robust features for the classification of sensor dat...
Recognition of daily human activities in various locomotion and transportation modes has numerous applications like coaching users for behavior modification and maintaining a healthy lifestyle. Besides, applications and user interfaces aware of user mobility through their smartphones can also aid in urban transportation planning, smart parking, and...
Early fall detection imposes a challenge for preventing life-threatening conditions to the health in geriatrics. As per the World Health Organization, around that half of total elderly individuals fall every year, which is considered a major cause of death. Fall-down detection and prediction is an active research topic to track seniors who reside a...
Human activity recognition and analysis have a great number of important applications in numerous fields including computer vision, ubiquitous computing, human-computer interactions, healthcare, robotics, and surveillance. Video-based and sensor-based human activity recognition have progressed tremendously in the last two decades. In this chapter,...
Emotion recognition and analysis is an essential part of affective computing which plays a vital role nowadays in healthcare, security systems, education, etc. Numerous scientific researches have been conducted developing various types of strategies, utilizing methods in different areas to identify human emotions automatically. Different types of e...
Monitoring human activities from a distance without actively interacting with the subjects to make a decision is a fascinating research domain given the associated challenges and prospects of building more robust artificial intelligence systems. In recent years, with the advancement of deep learning and high-performance computing systems, contactle...
Research in Activity Recognition is one of the thriving areas in the field of computer vision. This development comes into existence by introducing the skeleton-based architectures for action recognition and related research areas. By advancing the research into real-time scenarios, practitioners find it fascinating and challenging to work on human...
Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joi...
Sensor-based Human Activity Recognition (HAR) has been explored by many research communities and industries for various applications. Conventional pattern recognition approaches based on handcrafted features contributed a lot in this research field by employing general classification approaches. This chapter represents those handcrafted features in...
Cooking Activity Recognition Challenge [1] is organized as a part of ABC2020 [2]. In this work, we analyze and summarize the approaches of submissions of the Challenge. A dataset consisting of macro and micro activities, collected in a Cooking scenario were opened to the public with a goal of recognizing both of these activities. The participant te...
Activity recognition is one of the most researched topics in the field of machine learning-based recognition. There are many challenges associated with Human Activity Recognition. One of the most important challenges to overcome is the simultaneous recognition of complex activities as well as smaller activities that are part of such complex activit...
Detecting head- and mouth-related human activities of elderly people are very important for nurse care centers. They need to track different types of activities of elderly people like swallowing, eating, etc., to measure the health status of elderly people. In this regard, earable devices open up interesting possibilities for monitoring personal-sc...
This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in da...
The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems...
Focusing on the vision-based and sensor-based recognition and analysis of human activity and behavior, this book gathers extended versions of selected papers presented at the International Conference on Activity and Behavior Computing (ABC 2020), held in Kitakyushu, Japan on August 26 – 29, 2020. The respective chapters cover action recognition, ac...
This book is a truly comprehensive, timely, and very much needed treatise on the conceptualization of analysis, and design of contactless & multimodal sensor-based human activities, behavior understanding & intervention. From an interaction design perspective, the book provides views and methods that allow for more safe, trustworthy, efficient, and...
This book serves as the first guideline of the integrative approach, optimal for our new and young generations. Recent technology advancements in computer vision, IoT sensors, and analytics open the door to highly impactful innovations and applications as a result of effective and efficient integration of those. Such integration has brought to scie...
This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers...
Epilepsy is a frequently observed neurological abnormality. In the manual method, a physician monitors the recording of Electroencephalogram (EEG) of a patient to detect epileptic seizures. But this method is time-consuming and fallible. This chapter presents an automatic epileptic seizures detection and EEG signals classification method based on m...
Blindness prevents a person from gaining knowledge of the surrounding environment and makes unassisted navigation, object recognition, obstacle avoidance, and reading tasks a major challenge. In this work, we propose a novel visual aid system for the completely blind. Because of its low cost, compact size, and ease-of-integration, Raspberry Pi 3 Mo...
Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjec...
There is no doubt that the image has become the most common source of information in all ways as it carries a huge amount of natural descriptions about the corresponding scene. Recently, biomedical images-based applications have received more attention from the image processing and health informatics research communities. Principally, biomedical im...