A group of researchers from the Beijing Institute of Technology have developed a lower limb rehabilitation robot, as detailed in their recent publication in Cyborg Bionic Systems. The robot incorporates a novel force measurement approach from human-robot interactions, which markedly enhances the safety and effectiveness of gait training.
The researchers from the School of Artificial Intelligence, University of Chinese Academy of Sciences, have developed a lower limb rehabilitation robot that utilizes human-machine interaction-based measurements to adaptively improve patient engagement and safety during gait training. Image Credit: Fuyang Yu, School of Artificial Intelligence, University of Chinese Academy of Sciences.
While rehabilitation robots have been around for some time, recent advancements in their technology are pushing the boundaries of their effectiveness. Traditional rehabilitation robots often use fixed gait patterns that may not be suitable for all patients, particularly those with residual muscle strength. The new robot developed by the Beijing Institute of Technology team represents a significant move away from this model. It adjusts its gait dynamically in real time based on the user’s intent and physical capabilities, a totally different approach compared to the passive approaches commonly used.
Collaborating with institutions such as the University of Chinese Academy of Sciences and the Institute of Automation at the Chinese Academy of Sciences, the team designed a robot equipped with advanced sensors and control systems. These technologies work together to monitor the forces generated by a patient’s movements, allowing the robot to provide precise and adaptive support.
The robot's key innovation is its adaptive gait training capability. It uses cantilever beam force sensors mounted face-to-face to detect subtle forces exerted by the patient. This data helps a dynamic model adjust the robot’s movements in real time, enhancing both the responsiveness and effectiveness of the training process.
Extensive testing validated the design, showing that the robot could significantly improve the training experience by adapting to the patient’s muscle inputs. This adaptability helps prevent muscle atrophy and boosts overall recovery outcomes, making the robot a promising tool for areas with limited access to professional rehabilitation services.
By offering personalized training remotely, this robot has the potential to broaden access to high-quality rehabilitation services, which could be transformative in global health contexts. Robotics can advance patient care, offering new hope for those with lower limb impairments and setting a new standard for integrating intelligent technology into therapeutic practices. This adaptive approach may well become a cornerstone in future physical rehabilitation, leading to more effective and responsive treatments.
Journal Reference:
Yu, F., et al. (2024) Adaptive Gait Training for a Lower Limb Rehabilitation Robot Using Human-Robot Interaction Force. Cyborg and Bionic Systems. doi.org/10.34133/cbsystems.0115.