Hidden Markov Model for recognition of skeletal databased hand movement gestures

Cong Giao, Bui and Hoai An, Trinh Hoai and Thi Hong Anh, Nguyen and Nhut Minh, Ho (2018) Hidden Markov Model for recognition of skeletal databased hand movement gestures. EAI Endorsed Transactions on Context-aware Systems and Applications, 4 (14): e1. ISSN 2409-0026

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Abstract

The development of computing technology provides more and more methods for human-computer interaction applications. The gesture or motion of a human hand is considered as one of the most basic communications for interacting between people and computers. Recently, the release of 3D cameras such as Microsoft Kinect and Leap Motion has provided many advantage tools to explore computer vision and virtual reality based on RGB-Depth images. The paper focuses on improving approach for detecting, training, and recognizing the state sequences of hand motions automatically. The hand movements of three persons are recorded as the input of a recognition system. These hand movements correspond to five actions: sweeping right to left, sweeping top to bottom, circle motion, square motion, and triangle motion. The skeletal data of hand joint are collected to build an observation database. Desired features of each hand action are extracted from skeleton video frames by using the Principle Component Analysis (PCA) algorithm for training and recognition. A hidden Markov model (HMM) is applied to train the feature data and recognize various states of hand movements. The experimental results showed that the proposed method achieved the average accuracy nearly 95.66% and 91.00% for offline and online recognition, respectively.

Item Type: Article
Uncontrolled Keywords: Skeletal data, Hand movement recognition, PCA algorithm, HMM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 16 Sep 2020 08:29
Last Modified: 16 Sep 2020 08:29
URI: https://eprints.eudl.eu/id/eprint/304

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