Prediction of lower extremity multi-joint angles during overground walking by using a single IMU with a low frequency based on an LSTM recurrent neural network

Joohwan Sung, Sungmin Han, Heesu Park, Hyun Myung Cho, Soree Hwang, Jong Woong Park, Inchan Youn

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previ-ous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

Original languageEnglish
Article number53
Issue number1
Publication statusPublished - 2022 Jan 1

Bibliographical note

Funding Information:
Funding: This research was funded by the Korea Medical Device Development Fund grant funded by the MSIT (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (1711138169, KMDF KMDF_PR_20200901_0100), the National Research Council of Science and Technology (NST) grant by the Korea government (MSIT) (No. CAP-18014-000), and Korea Institute of Science and Technology Institutional Program (2E31153) Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Korea Institute of Science and Technology (IRB-2019-027, 1 August 2019).

Publisher Copyright:
© 2021 by the authors. Li-censee MDPI, Basel, Switzerland.


  • Deep neural network
  • Gait analysis
  • Inertial measurement unit
  • Long short-term memory
  • Wearable sensor

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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