Md. Atiqur Rahman Ahad

Md. Atiqur Rahman Ahad
University of Dhaka

Ph.D.(KyuTech), M.(UNSW), M.(DU), B.Sc.(Hons)(DU) [SM-IEEE; SM-OPTICA]
Weblink: http://ahadvisionlab.com

About

174
Publications
75,002
Reads
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1,546
Citations
Introduction
I'm a Professor, Univ of Dhaka, BD; & Specially Appointed Associate Professor, Osaka Univ, JP. I'm a Senior Member, IEEE; Senior Member, OPTICA. Personal site: http://ahadVisionLab.com I am less frequent at RG :) #Call for Papers: ICIEV - http://cennser.org/ICIEV IVPR - http://cennser.org/IVPR Journal - http://cennser.org/IJCVSP FB: https://www.facebook.com/AtiqAhad atiqahad [AT] du.ac.bd
Additional affiliations
November 2009 - March 2012
Kyushu Institute of Technology
Position
  • JSPS Postdoctoral Fellow; & Visiting Researcher
Description
  • I worked as a JSPS Postdoctoral Fellow for 2 years, and later as Visiting Researcher for 4 months, until March 2012.
December 2007 - February 2008
Nikon Corporation, Japan
Description
  • Nikon Corporation, Japan
March 2002 - July 2004
UNSW Sydney
Position
  • Casual Academic; & Student
Description
  • I did my Masters (2 years course) from the School of CSE, and I worked as Casual Academic for three terms
Education
October 2006
Kyushu Institute of Technology
Field of study
  • Computer Vision
March 2002
UNSW Sydney
Field of study
  • School of CSE
July 1996
University of Dhaka
Field of study
  • APECE

Publications

Publications (174)
Chapter
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...
Chapter
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...
Chapter
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...
Chapter
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....
Book
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...
Conference Paper
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,...
Article
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
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...
Article
Full-text available
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...
Article
Full-text available
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...
Chapter
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...
Chapter
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...
Chapter
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...
Chapter
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...
Chapter
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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,...
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...
Chapter
Full-text available
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...
Chapter
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...
Article
Full-text available
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...
Chapter
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...
Chapter
Full-text available
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...
Chapter
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...
Chapter
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...
Article
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...
Book
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...
Book
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...
Book
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...
Book
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...
Book
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...
Chapter
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...
Article
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...
Preprint
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...
Chapter
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...
Chapter
Full-text available
Health Informatics (HI) imposes informatics concepts, theories, and practices to real-life circumstances to attain improved health outcomes, which incorporate not only collection and accumulation of data but also data analysis and presentation. A plethora of research activities has been going on in the field of HI due to the widespread of its proba...
Conference Paper
Full-text available
Nurse care activity recognition is a new challenging research field in human activity recognition (HAR) because unlike other activity recognition, it has severe class imbalance problem and intra-class variability depending on both the subject and the receiver. In this paper, we applied the Random Forest-based resampling method to solve the class im...
Conference Paper
Full-text available
Nursing activity recognition adds a new dimension to the healthcare automation system. But nursing activity recognition is very challenging than identifying simple human activities like walking, cycling, swimming, etc. due to intra-class variability between activities. Besides, the lack of proper dataset does not allow researchers to develop a gene...
Conference Paper
The Sussex-Huawei Locomotion-Transportation (SHL) Challenge 2020 was an open competition of recognizing eight different activities that had been performed by three individual users and participants of this competition were tasked to classify these eight different activities with modes of locomotion and transportation. This year's data was recorded...
Conference Paper
Full-text available
In this paper, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository, and UCI Machine Learning Repository, is shown. Applying transfer learning and CNN-LSTM neural network architectures, we find out a suitable control scheme for a myoelectric prosthetic hand...
Conference Paper
Acoustic Myogram (AMG) is the vibration or sound signal produced during muscle contraction and relaxation. A simple system like a condenser microphone is enough to capture an AMG signal from muscles, unlike complex systems that are used in surface Electromyography (sEMG). Moreover, AMG signal is not highly sensitive to sensor placement like sEMG si...
Cover Page
Full-text available
Call for quality book chapter, Springer [Free, SCOPUS-indexed]: "Vision, Sensing and Analytics: Integrative Approaches" https://lnkd.in/d4VjMwX Editors: Myself & Prof. Atsushi Inoue Recent technology advancements in vision, sensing and analytics have brought to us the new trends and made significant impacts in our societies. Especially the advancem...
Article
Full-text available
Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern f...
Chapter
Human activity analysis and recognition tasks can be considered as classification problems in most of the cases. This chapter represents the overview of classification problems explaining their different types with examples. Binary classification, multilabel classification, multi-class classification, and hierarchical classification tasks are prese...
Chapter
Sensor-based Human Activity Recognition (HAR) has been explored by many research communities and industries for various applications. In the earlier chapters, we have presented methodologies to accomplish human activity recognition, pre-processing steps of raw data from sensors, segmentation of these data using various windowing approaches, feature...
Chapter
The field of human activity recognition (HAR) using different sensor modalities poses numerous challenges to the researchers working in this domain. Though traditional pattern recognition approaches performed well in this regard earlier, the cost of poor generalization and the cost of shallow learning due to the handcrafted features have opened a n...
Chapter
Sensory modality is a primary concern in sensor-based activity recognition research. The usage of wearable devices and utilizing embedded smartphone sensor data to recognize daily activities has become famous in this research field nowadays. This chapter deals with the challenges of choosing an appropriate sensing device and application tools for d...
Chapter
Human Activity Recognition (HAR) has explored a lot recently in the academia and industries for numerous applications. There are lots of progress in the domain of vision-based action or activity recognition due to the advent of deep learning models and due to the availability of very large datasets in the last several years. However, there are stil...
Chapter
Automatic recognition of human activities using sensor-based systems is commonly known as human activity recognition (HAR). It is required to follow a structural pipeline to recognize activity using a machine learning technique. This chapter represents the different stages of this structural pipeline in detail. Following this, the preprocessing ste...
Chapter
Human Activity Recognition (HAR) using installed sensors has made renowned progress in the field of pattern recognition and human-computer interaction. To make efficient machine learning models, researchers need publicly available benchmark datasets. In this chapter, we have bestowed a comprehensive survey on sensor-based benchmark datasets. We hav...
Chapter
The constant growth of sensor-based systems and technologies for the detection of human activities has made notable progress in the field of human-computer interaction. The continuation of Internet connectivity into daily objects and physical devices has made it possible for the researchers to use IoT sensors for healthcare, elderly people monitori...
Chapter
After building the model to recognize activities from sensor data, it is essential to investigate the effectiveness of the model. The evaluation of the performance for machine learning methods can be performed using some evaluation matrices. This chapter properly explains the evaluation matrices namely accuracy, precision, recall, F1 score, balance...
Article
Full-text available
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the...
Chapter
Human action or activity or behavior analysis, recognition, and understanding are very important research areas in the field of computer vison, internet of things (IoT) sensor-based analysis, human-computer interaction (HCI), affective computing, intelligent system, healthcare facilities, and so on. There is much importance in human action recognit...
Article
Full-text available
Abstract Gait-based features provide the potential for a subject to be recognized even from a low-resolution image sequence, and they can be captured at a distance without the subject’s cooperation. Person recognition using gait-based features (gait recognition) is a promising real-life application. However, several body parts of the subjects are o...
Conference Paper
Full-text available
The goal of the SHL recognition challenge 2019 is to recognize transportation modalities in a sensor placement independent manner. In this paper, the performance of shallow neural networks is benchmarked by Team Orion in such a manner on the dataset provided in the challenge, using 156 handcrafted temporal and spectral features per sensor through t...
Conference Paper
Full-text available
Sensor-based recognition of locomotion and transportation modes has numerous application domains including urban traffic monitoring, transportation planning, and healthcare. However, the use of a smartphone in a fixed position and orientation in previous research works limited the user behavior a lot. Besides, the performance of naive methods for p...
Conference Paper
Wearable sensors are monumental for human activity recognition. Researchers are continuously inventing new technology to detect human activity properly. Earable opens up interesting possibilities of monitoring personal scale behavioral activities. In this paper, we explore earables device 'eSense' multisensory stereo device for personal scale behav...
Chapter
Full-text available
Cyber-Physical Systems (CPS) is feedback systems that are a concoction of closely integrated physical processes, communication, and computation which interacts with the human through various modalities. It is developed as the censorious infrastructure in the different implementing platform and has the perspective to influence our day to day life. T...