Hidden Markov Model for recognition of skeletal databased hand movement gestures

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.

Authors and Affiliations

Bui Cong Giao, Trinh Hoai An, Nguyen Thi Hong Anh, Ho Nhut Minh

Keywords

Related Articles

Fast Radial and Bilateral Symmetry Detection Using Inverted Gradient Hash Maps

This paper presents a fast and novel algorithm for both radial and bilateral symmetry detection based on inverted gradient hash maps (IGHMs). A hash map is an associative array that stores image gradient magnitudes and o...

Products, Coproducts and Universal Properties of Autonomic Systems

Self-* is widely considered as a foundation for autonomic computing. The notion of autonomic systems (ASs) and self-serves as a basis on which to build our intuition about category of ASs in general. In this paper we wil...

Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems

A context-aware system uses context to provide relevant information and services to the user, where relevancy depends on the user’s situation. This relevant information could include a wide range of heterogeneous content...

Design guidelines for rapid and simple context-aware mobile application development – an android case study

Presenting a context-aware service and information is a key aspect of ubiquitous computing, but development of such applications is quite complicated. Context-aware applications should be able to obtain raw data fromsens...

Hidden Markov Model for recognition of skeletal databased hand movement gestures

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 interact...

Download PDF file
  • EP ID EP45803
  • DOI http://dx.doi.org/10.4108/eai.18-6-2018.154819
  • Views 294
  • Downloads 0

How To Cite

Bui Cong Giao, Trinh Hoai An, Nguyen Thi Hong Anh, Ho Nhut Minh (2017). Hidden Markov Model for recognition of skeletal databased hand movement gestures. EAI Endorsed Transactions on Context-aware Systems and Applications, 4(14), -. https://europub.co.uk./articles/-A-45803