A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements. (27th October 2021)

• Record Type: Journal Article
• Authors: Zheng, Yuhuang
• Other Names: Li Yang Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2021(2021)
• Publication Date: 2021-10-27
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2021/9472053
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.