Indexed by:期刊论文

Date of Publication:2016-10-01

Journal:IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS

Included Journals:SCIE、EI、Scopus

Volume:46

Issue:5

Page Number:769-775

ISSN No.:2168-2291

Key Words:Badminton stroke recognition; body sensor networks (BSN); hidden Markov model (HMM)

Abstract:A badminton training system based on body sensor networks has been proposed. The system may recognize different badminton strokes of badminton players. A two-layer hidden Markov model (HMM) classification algorithm is proposed to recognize 14 types of badminton strokes. In the first layer, we use acceleration magnitude of the right wrist to determine a threshold to detect strokes, and then, the HMM is applied to filter out nonstroke motions. In the second layer, we adopt the HMM to classify all the strokes into 14 categories. Experimental results show that the two-layer HMM can achieve good recognition accuracy. The effectiveness and feasibility of the two-layer HMM classification algorithm have been verified in a comparison.