Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Wujing Cao1,2; Xinbo Zhu3; Rui Xu3; Changyu Li4; Hongliu Yu5; Mingming Zhang6; Haoyong Yu7; Worawarit Kobsiriphat8; Xinyu Wu1,2

10.1109/ARSO60199.2024.10557800

2024-05-22

2162-7568

979-8-3503-4464-6

2024 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)

20-22 May 2024

Hong Kong

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

其他

CPCI-S ; EI

1.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2.SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems.
3.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
4.Jining Zhongke Intelligent Technology Co., Ltd.
5.University of Shanghai for Science and Technology
6.Southern University of Science and Technology
7.Department of Biomedical Engineering, National University of Singapore.
8.National Metal and Materials Technology Center, Thailand

Wujing Cao,Xinbo Zhu,Rui Xu,et al. Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise[C],2024.