Implementation of dynamic time warping for gesture recognition in sign language using high performance computing

Salagar, Muaaz; Kulkarni, Pranav; Gondane, Saurabh

doi:10.1109/ichci-ieee.2013.6887814

Cited by 3 publications

(2 citation statements)

References 5 publications

(1 reference statement)

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“…Kinect can detect hand motions and hand places. Muaaz et al [16] developed a system that can recognize American sign language with Kinect. This system has a high average recognition rate of 80%.…”

Section: Introductionmentioning

confidence: 99%

Data Glove with Bending Sensor and Inertial Sensor Based on Weighted DTW Fusion for Sign Language Recognition

Amino

Lei

2023

Electronics

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There are numerous communication barriers between people with and without hearing impairments. Writing and sign language are the most common modes of communication. However, written communication takes a long time. Furthermore, because sign language is difficult to learn, few people understand it. It is difficult to communicate between hearing-impaired people and hearing people because of these issues. In this research, we built the Sign-Glove system to recognize sign language, a device that combines a bend sensor and WonderSense (an inertial sensor node). The bending sensor was used to recognize the hand shape, and WonderSense was used to recognize the hand motion. The system collects a more comprehensive sign language feature. Following that, we built a weighted DTW fusion multi-sensor. This algorithm helps us to combine the shape and movement of the hand to recognize sign language. The weight assignment takes into account the feature contributions of the sensors to further improve the recognition rate. In addition, a set of interfaces was created to display the meaning of sign language words. The experiment chose twenty sign language words that are essential for hearing-impaired people in critical situations. The accuracy and recognition rate of the system were also assessed.

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Section: Introductionmentioning

confidence: 99%

Data Glove with Bending Sensor and Inertial Sensor Based on Weighted DTW Fusion for Sign Language Recognition

Amino

Lei

2023

Electronics

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“…By doing this, different activities can be mapped to many human models with various mesh topologies. After a detailed research on aforementioned as well as a few other works like in [8], [9], [10], [11], we decided to go with Kinect as our hardware and HMM as a machine learning technique.…”

Section: Related Workmentioning

confidence: 99%

Virtual Personal Trainer using Microsoft Kinect and Machine Learning

Rane¹,

Potnis²,

Sansare³

et al. 2018

IJCA

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Human-Computer Interaction is a flourishing area in terms of research and has many real-world applications. Keeping this in mind, we came up with an idea to develop HumanComputer interaction for proper conduct of physical exercises at home, using information sensed by an RGB-D camera, namely the Microsoft Kinect. Along with Kinect, we also make use of a Machine Learning technique to perform operations on captured data to predict the accuracy of a performed physical exercise. Our approach is based on the study of the movement of various joints in the human body, which we examine with the use of the Kinect. We take into account an algorithm for our implementation -Hidden Markov Model (HMM). We combine these and detect the posture of a user while he performs a particular exercise, before comparing it with our ideal database of postures. Based on this comparison, we predict the accuracy of the exercise and aim to improve and correct the form of the user in terms of performance of the exercise.

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