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( Md. Zia Uddin ),( Jaehyoun Kim ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.2
Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people`s behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.
Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models
( Md. Zia Uddin ),( Jaehyoun Kim ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.6
Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.
A Local Feature-Based Robust Approach for Facial Expression Recognition from Depth Video
( Md. Zia Uddin ),( Jaehyoun Kim ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.3
Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person`s face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.
Zia Uddin, Md.,Kim, Tae-Seong,Jeong Tai Kim, SAGE Publications 2011 Indoor + built environment Vol.20 No.1
<P> Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future. </P>
Uddin, Md. Zia,Kim, Jeong Tai,Kim, Tae-Seong SAGE Publications 2014 Indoor and Built Environment Vol.23 No.1
<P>Gait recognition at smart home is considered as a primary function of the smart system nowadays. The significance of gait recognition is high especially for the elderly as gait is one of the basic activities to promote and preserve their health. In this work, a novel method was proposed for human gait recognition by processing depth videos from a depth camera. The gait recognition method utilizes local directional patterns (LDPs) for local feature extraction from depth silhouettes and hidden Markov models (HMMs) for recognition. The LDP features were first extracted from the depth silhouettes of a human body from each frame of a video containing human gait. The dimension of the LDP features was reduced by principal component analysis. Then, each HMM was trained using the LDP features. Finally, the recognition was done with a maximum likelihood calculation of the trained HMMs of different gaits. We focused on training and recognizing two kinds of gaits here, namely, normal and abnormal. The proposed approach shows superior recognition performance over other traditional methods of gait recognition.</P>
Edge-of-things computing framework for cost-effective provisioning of healthcare data
Alam, Md. Golam Rabiul,Munir, Md. Shirajum,Uddin, Md. Zia,Alam, Mohammed Shamsul,Dang, Tri Nguyen,Hong, Choong Seon Elsevier 2019 Journal of parallel and distributed computing Vol.123 No.-
<P><B>Abstract</B></P> <P>Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient’s body area networks (BAN) and patients’ medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Edge-of-Things (EoT) computation framework for healthcare service provisioning. </LI> <LI> Portfolio optimization approach for cost-effective healthcare data provisioning. </LI> <LI> Alternating direction method of multipliers (ADMM) for healthcare data offloading. </LI> </UL> </P>