About this Research Topic
Wearable assistive robotic technologies such as prosthetics and exoskeletons have been developed to enhance gait and mobility in healthy people and individuals with mobility deficits. They can be utilized as rehabilitative and therapeutic devices to aid in the recovery of individuals with muscular and neurological disorders. Additionally, wearable assistive devices have also been designed to augment human performance by facilitating activities that require less energy expenditure. However, some common issues with wearable robotic devices are their bulky designs as well as the challenges in adapting and learning to use them. Given the gait-to-gait and person-to-person variations, the generalized control parameters for different users may not be appropriate to get the best control performance. Therefore, customization/personalization of the assistance control for wearable robotic devices is necessary.
The appeal of modern powered wearable robotic devices, as opposed to passive devices, is their intelligent, active components that can be programmed to provide external assistance in physical therapy or daily living for various populations. This feature transforms the function of wearable devices into movement augmentation or physical rehabilitation and opens up the opportunity to emphasize the personalized treatment or assistance that considers individual variability. Take lower limb wearable robotic devices as an example, while the current development has become clinically available, it is still challenging to accommodate the gait-to-gait or day-to-day inter-human variations or intra-human physical capability variations according to the current state of the art. The underlying reason lies in the physiological and neurological differences between times on the same person or between individuals that can cause divergent responses to a generalized controller, which means one participant's optimal control strategy may perform poorly when applied to other participants. Therefore, a participant-specific approach may be required when considering how to get optimal control parameters for individualized assistance from robotic devices.
The scope of this Research Topic is the robotic assistance personalization for human locomotion tasks from either lower-limb exoskeletons or prosthetics.
Related to the human-machine-interaction system, topics of interest include, but are not limited to, the following:
• what are the control objectives
• optimization approach
• control parameters
• cost function formulation
• machine learning
Research articles and review articles are welcome.
Qiang Zhang
The University of North Carolina at Chapel Hill and North Carolina State University
Research interests: control of rehabilitative/assistive robotic devices with particular interests in
exoskeletons and functional electrical stimulation (FES), Lyapunov-based nonlinear control and adaptive control, machine learning-based control, human-robot interaction, human motion intent prediction neuromusculoskeletal modeling and control, neuromuscular signals processing (surface electromyography and ultrasound imaging), human locomotion analysis and biomechanics, sensor fusion, and linear/nonlinear observer design.
The appeal of modern powered wearable robotic devices, as opposed to passive devices, is their intelligent, active components that can be programmed to provide external assistance in physical therapy or daily living for various populations. This feature transforms the function of wearable devices into movement augmentation or physical rehabilitation and opens up the opportunity to emphasize the personalized treatment or assistance that considers individual variability. Take lower limb wearable robotic devices as an example, while the current development has become clinically available, it is still challenging to accommodate the gait-to-gait or day-to-day inter-human variations or intra-human physical capability variations according to the current state of the art. The underlying reason lies in the physiological and neurological differences between times on the same person or between individuals that can cause divergent responses to a generalized controller, which means one participant's optimal control strategy may perform poorly when applied to other participants. Therefore, a participant-specific approach may be required when considering how to get optimal control parameters for individualized assistance from robotic devices.
The scope of this Research Topic is the robotic assistance personalization for human locomotion tasks from either lower-limb exoskeletons or prosthetics.
Related to the human-machine-interaction system, topics of interest include, but are not limited to, the following:
• what are the control objectives
• optimization approach
• control parameters
• cost function formulation
• machine learning
Research articles and review articles are welcome.
Qiang Zhang
The University of North Carolina at Chapel Hill and North Carolina State University
Research interests: control of rehabilitative/assistive robotic devices with particular interests in
exoskeletons and functional electrical stimulation (FES), Lyapunov-based nonlinear control and adaptive control, machine learning-based control, human-robot interaction, human motion intent prediction neuromusculoskeletal modeling and control, neuromuscular signals processing (surface electromyography and ultrasound imaging), human locomotion analysis and biomechanics, sensor fusion, and linear/nonlinear observer design.
Keywords: Wearable robotics, Human-in-the-loop, Human-machine-interaction, Locomotion assistance, Optimal control
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