Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Sutopo, Joko

doi:10.30534/ijatcse/2019/85862019

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Without using markers, this depth sensor can track body movement using its depth, red, green, and blue (RGB) and infrared sensor to extracts a 3D virtual skeleton of the body [53]. From the depth image and with customable function, this device can provide in detail the value of the 20 body joints [54]. A recent study by Mohsen et al also utilized the 3D depth sensors to capture skeleton data.…”

Section: Proposed Approachmentioning

confidence: 99%

Experimental Approach in Gait Analysis and Classification Methods for Autism Spectrum Disorder: A Review

Zakaria¹

2020

IJATCSE

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Recently, studies in autism have gained much attention. Hence, for this study, experimental methods for gait analysis in autism children done by previous researchers were reviewed prior to our proposed method. Although gait is not one of the diagnostic criteria for autism spectrum disorder (ASD), existences of motor deficits specifically abnormal gait patterns during walking could be investigated and explored further. Thus, the purpose of this study is to conduct a thorough review by previous researches related to walking gait for ASD children with specific scope defined. Here, the discussion focused on the sample sizes or subjects' database, gait analysis methods utilized, type of gait features extracted and the method used for assessment of the walking gait in ASD. At the end of this review, a new markerless based approach is proposed that will be more suitable to be used for detection of anomaly gait in children with autism specifically outside the laboratory environment. The proposed method will be based on the benchmark model of a 3D marker-based model and the proposed markerless model will be using a depth camera that will provide the three-dimensional (3D) skeleton image of the subject along with the motion sensor for data acquisition purpose.

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Section: Proposed Approachmentioning

confidence: 99%

Experimental Approach in Gait Analysis and Classification Methods for Autism Spectrum Disorder: A Review

Zakaria¹

2020

IJATCSE

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“…As we know, machine learning algorithms is used for computational methods in learning the information directly from the data without relying on a predetermined equation and adaptively improve its performance as the number of data available during the learning process increased. Some well-known classifiers namely artificial neural network (ANN), support vector machine (SVM) and Naïve Bayes (NB) are employed for recognition and classification of broad areas in pathological gait such as in Parkinson's disease [21] & [22], stroke patients [23], cerebral palsy [24], age-dependent gait [25] & [26], persons with gait disorders [18] & [27] as well as for recognition-based studies such as posture [28], walking [29] and fall detection [30].…”

Section: Asd Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysismentioning

confidence: 99%

ASD Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysis

Zakaria¹

2020

IJETER

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The aim of this study is to explore the potential of the markerless-based gait features using both unsupervised and supervised algorithms namely the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) as feature extraction techniques. Firstly, a depth camera that created the three-dimensional (3D) skeleton image of the subject upon detection of movements by the motion sensor is used as data acquisition device in acquiring the walking gait of 30 TD and 23 ASD group. Next, the extracted gait features are translated into two categories named as Direct Joint Feature (DIR) and Reference Joint Feature (REF) features. To evaluate the effectiveness of PCA and LDA as feature extraction, Support Vector Machine (SVM), Naïve Bayes Classifier (NBC) and Artificial Neural Network (ANN) are chosen as classifiers. Overall results showed that LDA is the most suitable feature extraction with accuracy of 99.33% using NBC as classifier with the DIR feature dataset as inputs.

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“…For dynamic signals, the hand motion should be identified and followed. For hand following, either the video is separated into outlines and each casing is prepared alone, or some following subtleties like shape or skin shading utilizing a few apparatuses [4].…”

Section: Literature Surveymentioning

confidence: 99%

Human Computer Interaction using Hand Gesture Recognition

Selvarathi¹

2020

IJATCSE

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Hand gesture recognition provides an interaction between human and PCs. Its applications extend from therapeutic recovery to purchaser gadgets control (for example cell phone). To identify hand gestures, different methods are used. Signals a non-verbal type of correspondence gives the HCI interface. Ongoing vision-based hand motion acknowledgment is viewed as increasingly more possible for HCI with the assistance of most recent advances in the field of PC vision. This venture manages conversation of different procedures, techniques and calculations identified with the motion acknowledgment. The hand motion is the most simple and common method for correspondence. Hand motion acknowledgment has the different favorable circumstances of ready to speak with the Technology through fundamental communication via gestures. The motion will ready to diminish the utilization of most noticeable equipment gadgets which are utilized to control the exercises of the PC.

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Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Cited by 6 publications

References 17 publications

Experimental Approach in Gait Analysis and Classification Methods for Autism Spectrum Disorder: A Review

Experimental Approach in Gait Analysis and Classification Methods for Autism Spectrum Disorder: A Review

ASD Children Gait Classification Based On Principal Component Analysis and Linear Discriminant Analysis

Human Computer Interaction using Hand Gesture Recognition

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