Advancements in Human-Robot Interaction for Rehabilitation

The field of rehabilitation technology is shifting towards a more integrated approach, combining the strengths of robotic systems with the clinical expertise of therapists. This is evident in the development of novel paradigms such as physical Human-Robot-Human Interaction (pHRHI), which enables bidirectional interaction between therapists and patients, allowing for more effective and personalized rehabilitation. Another key area of development is the use of machine learning and reinforcement learning to improve the control and adaptability of robotic systems, such as exoskeletons and wheelchairs. These advancements have the potential to enhance mobility and autonomy for individuals with motor impairments. Noteworthy papers in this area include:

  • A study on pHRHI, which demonstrated improved rehabilitation outcomes for chronic stroke patients.
  • A paper on Hierarchical Reinforcement Learning for adaptive walking control, which showed increased overall network accuracy and terrain-specific performance increases.
  • A study on shared control of holonomic wheelchairs through reinforcement learning, which ensured collision-free navigation and smart orientation of the wheelchair.

Sources

Therapist-Exoskeleton-Patient Interaction: An Immersive Gait Therapy

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals

Shared Control of Holonomic Wheelchairs through Reinforcement Learning

An Exploratory Study on Human-Robot Interaction using Semantics-based Situational Awareness

Robot-mediated physical Human-Human Interaction in Neurorehabilitation: a position paper

Event Detection for Active Lower Limb Prosthesis

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