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Title: HMM-based IMU data processing for arm gesture classification and motion tracking

Authors: Danping Wang; Jina Wang; Yang Liu; Xianming Meng

Addresses: School of Information Engineering, Shenyang University, Shenyang, 110044, China ' Liaoning Province's Construction and Engineering Center for Advanced Equipment Manufacturing Base, Shenyang, 110001, China; Liaoning Information Security and Software Testing and Certification Center, Shenyang, 110001, China ' School of Information Engineering, Shenyang University, Shenyang, 110044, China ' Wuxi Forward Technology Co., Ltd, Wuxi, 214191, China

Abstract: This paper investigates inertial measurement unit (IMU) data processing methods for human gesture classification and arm motion tracking in wireless body sensor network (WBSN). The method is adopted that consists of two main stages. In the training stage, the supervised learning method is adopted to obtain the HMM model and the Viterbi algorithm is used to obtain the optimal hidden state sequence in the testing stage. HMM also complements the intuitional evaluation for arm motion recovery. We take advantage of the twists and exponential maps to recover the arm motion process. In addition, visual tracking device-VICON is utilised to validate the accuracy of the inertial tracking system. The experimental results show that the HMM algorithm gesture classifier achieves up to 96.63% accuracy on five commonly used arm gestures and visual assisted tracking outcomes verify the robustness and feasibility of the IMU tracking device.

Keywords: gesture classification; arm motion tracking; inertial measurement unit; computer network; vision motion tracking.

DOI: 10.1504/IJMIC.2023.128767

International Journal of Modelling, Identification and Control, 2023 Vol.42 No.1, pp.54 - 63

Received: 25 Nov 2021
Accepted: 04 Feb 2022

Published online: 03 Feb 2023 *

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