Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance

Qiang Zhang, Varun Nalam, Xikai Tu, Minhan Li, Jennie Si, Michael D. Lewek, He Helen Huang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Recently, human-in-the-loop (HIL) optimization has been investigated for providing assistance to minimize energetic expenditure, usually quantified by metabolic cost. However, this full-body global effect evaluation may not directly reflect the local functions of the targeted joint(s). This makes it difficult to assess the direct effect when robotic assistance is provided. In addition, the HIL optimization method usually does not take into account local joint trajectories, a consideration that is important in imposing healthy joint movements and gait patterns for individuals with lower limb motor deficits. In this paper, we propose a model-free reinforcement learning (RL)-based control framework to achieve a normative range of motion and gait pattern of the hip joint during walking. Our RL-based control provides personalized assistance torque profile by heuristically manipulating three control parameters for hip flexion and extension, respectively, during walking. A least square policy iteration was devised to optimize a cost function associated with control efforts and hip joint trajectory errors by tuning the control parameters. To evaluate the performance of the design approach, a compression sleeve was used to constrain the hip joint of unimpaired human participants to simulate motor deficits. The proposed RL control successfully achieved the desired goal of enlarging the hip joint's range of motion in three participants walking on a treadmill.

Original languageEnglish (US)
Pages (from-to)11126-11133
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 1 2022


  • Assistance personalization
  • locomotion
  • policy iteration
  • prosthetics and exoskeletons
  • rehabilitation robotics
  • reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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