Wearable assistive robotic technologies such as prosthetics and orthoses have been developed to enhance human mobility functions in healthy people and individuals with mobility deficits. Those robotic devices can be utilized as external power sources to augment human movement performance and reduce biological energy expenditure, as well as rehabilitative and therapeutic machines to aid the functions recovery of people with neurological or muscular disorders. These devices can be categorized into passive and active types, where passive devices have less output power and limited control performance when compared to active devices. The attraction of contemporary active wearable robotic devices lies in their smart and dynamic constituents that can be customized for dispensing external support during physical therapy or day-to-day activities across diverse demographics. As a result, wearable devices' functionality evolves into enhancing motion or aiding in physical recovery, opening the door to tailoring treatments or aid based on individual differences.
However, given the complex human-robot interaction system and person-to-person variabilities, active devices encounter a common challenge of achieving the best control performance. For example, after some time-consuming tuning of control parameters for one user, the same set of optimal control parameters may not be appropriate for other human users, or at least the control performance may not be optimal for others. Therefore, one key solution to address this challenge is to customize/personalize robotic control parameters to provide the optimal assistance for each user, which will contribute to maximizing the assistance benefits for either unaffected users or individuals with mobility deficits.
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.
Wearable assistive robotic technologies such as prosthetics and orthoses have been developed to enhance human mobility functions in healthy people and individuals with mobility deficits. Those robotic devices can be utilized as external power sources to augment human movement performance and reduce biological energy expenditure, as well as rehabilitative and therapeutic machines to aid the functions recovery of people with neurological or muscular disorders. These devices can be categorized into passive and active types, where passive devices have less output power and limited control performance when compared to active devices. The attraction of contemporary active wearable robotic devices lies in their smart and dynamic constituents that can be customized for dispensing external support during physical therapy or day-to-day activities across diverse demographics. As a result, wearable devices' functionality evolves into enhancing motion or aiding in physical recovery, opening the door to tailoring treatments or aid based on individual differences.
However, given the complex human-robot interaction system and person-to-person variabilities, active devices encounter a common challenge of achieving the best control performance. For example, after some time-consuming tuning of control parameters for one user, the same set of optimal control parameters may not be appropriate for other human users, or at least the control performance may not be optimal for others. Therefore, one key solution to address this challenge is to customize/personalize robotic control parameters to provide the optimal assistance for each user, which will contribute to maximizing the assistance benefits for either unaffected users or individuals with mobility deficits.
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.