Sriparna Saha

Learn More
The aim of this novel work is to recognize 12 health care linked gestures from young individuals of 20-40 years of age group. Due to constant sitting in a specific posture for deskbound jobs, functioning of joints and muscles of persons are deteriorated. The scope of this work is to recognize the early stage symptoms of those physical disorders and notify(More)
This work proposes gesture recognition algorithm for Indian Classical Dance Style using Kinect sensor. This device generates the skeleton of human body from which twenty different junction 3-dimensional coordinates are obtained. Here we require only eleven coordinates for the proposed work. Basically six joints coordinates about right and left hands and(More)
This novel work is aimed at the study of emotion recognition from gestures using Kinect sensor. The Kinect sensor along with Software Development Kit (SDK) generates the human skeleton represented by 3-dimensional coordinates corresponding to twenty body joints. Using the co-ordinates of eleven such joints from the upper body and the hands, a set of nine(More)
A simple method to detect gestures revealing muscle and joint pain is described in this paper. Kinect Sensor is used for data acquisition. This sensor only processes twenty joint coordinates in three dimensional space for feature extraction. The recognition part is achieved using a neural network optimized by Levenberg-Marquardt learning rule. A high(More)
— This work aims at designing a fuzzy matching algorithm that would automatically recognize an unknown ballet posture from seventeen fundamental ballet dance primitives. A novel and simple 7-stage system is proposed to achieve the desired objective. Minimized skeletons of the dance postures are generated after performing skin color segmentation on them.(More)
This paper presents a method for automatic hand gesture recognition of 'Bharatanatyam' dance using Fuzzy L membership function based approach. Here, a 3-stage system has been designed. In the first stage, the hand of the dancer from background is isolated using Texture based segmentation and thus the contour of the hand is extracted by using Sobel edge(More)
The paper presents a comparison between different membership functions based type-1 fuzzy set for automatic hand gesture recognition for American Sign Language recognition. First pre-processing of the images is done using skin color based segmentation, morphological operations and to extract the hand gesture image from the background, Sobel edge detection(More)
—Sign language interpretation is gaining a lot of research attention because of its social contributions which is proved to be extremely beneficial for the people suffering from hearing or speaking disabilities. This paper proposes a novel image processing sign language detection framework that employs MAdaline network for classification purpose. This paper(More)