Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.

Journal: Biomedical engineering online
PMID: 40176123

Abstract

BACKGROUND: Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawling devices for rehabilitation training was still in its early stages. In particular, precisely generating motion trajectories is a prerequisite to controlling exoskeleton assistive devices, and deep learning-based prediction algorithms, such as Long-Short-Term Memory (LSTM) networks, have proven effective in forecasting joint trajectories of gait. Despite this, no previous studies have focused on forecasting the more variable and complex trajectories of infant crawling. Therefore, this paper aims to explore the feasibility of using LSTM networks to predict crawling trajectories, thereby advancing our understanding of how to actively control crawling rehabilitation training robots.

Authors

  • Jieyi Mo
    Department of Biomedical Engineering, Nanchang Hangkong University, Jiangxi, China.
  • Qiliang Xiong
    Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China.
  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Yuan Liu
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
  • Xiaoying Wu
    Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China.
  • Nong Xiao
  • Wensheng Hou
    Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, 400044, P.R. China. w.s.hou@cqu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals