Forecasting motion trajectories of elbow and knee joints during infant crawling based on long–short-term memory (LSTM) networks
This study demonstrates the feasibility of forecasting infant crawling trajectories using LSTM networks, which could potentially integrate with exoskeleton control systems. It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective control strategies to improve assistive crawling devices.
Authors
- Qiliang Xiong (ORCID: https://orcid.org/0000-0003-3056-7000)
- Jieyi Mo
- Wensheng Hou (ORCID: https://orcid.org/0000-0002-2201-4177)
- Xiaoying Wu (ORCID: https://orcid.org/0000-0002-6974-6041)
- Ying Chen (ORCID: https://orcid.org/0000-0001-7754-2367)
- Yuan Liu (ORCID: https://orcid.org/0000-0001-6441-2205)
- Nong Xiao (ORCID: https://orcid.org/0000-0002-6980-5013)
Institutions
- Chongqing University (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
- Nanchang Hangkong University (CN)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2025-04-02
- DOI
- https://doi.org/10.1186/s12938-025-01360-1
- Citations
- 1
- Primary Topic
- Prosthetics and Rehabilitation Robotics
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangxi Province