A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs

Minjae Kim, Levi J. Hargrove*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.

Original languageEnglish (US)
Article number1064313
JournalFrontiers in Neurorobotics
StatePublished - Jan 5 2023


  • benchmark data
  • continuous gait recognition
  • deep learning
  • gait phase
  • prosthetics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering


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