Abstract
Intelligent control strategies for active biomimetic prostheses could exploit the inter-joint coordination of limbs in human gait in order to mimic the functioning of a biological joint. A machine learning regression model could be employed to learn an input-output relationship between the coordinated limb motion in human gait and predict the motion of a particular limb/joint given the motion of other limbs/joints. Such a model could be potentially used as a controller for an intelligent prosthesis which aims to restore the functioning similar to an intact biological joint. For this, the model needs to be tailored for each user by learning the gait pattern specific to the user. The challenge of training such machine learning regression models in prosthetic control is that, the desired reference output cannot be obtained from an amputee due to the missing limb. In this study, we investigate the feasibility of using two different methods for training a random forest algorithm using incomplete amputee-specific data to predict the ankle kinematics and dynamics from hip, knee, and shank kinematics. First is an inter-subject approach which learns a generalized input-output relationship from a group of able-bodied individuals and then applies this generalized relationship to amputees. Second is a subject-specific approach which maps the amputee's inputs to a desired normative reference output calculated from able-bodied individuals. The subject-specific model outperformed the inter-subject model in predicting the ankle angle and moment in most cases and can be potentially used for devising a control strategy for an intelligent biomimetic ankle.
Highlights
Lower limb amputation hinders the quality of life
We examined how the type of training affects the performance of a random forest model when predicting θankle and τankle for 30 able-bodied and two unilateral transtibial amputees from the residual limb kinematics during walking
Since a purely subject-specific model is not possible for the transtibial amputees due to unavailability of the desired reference output, we proposed a modified subject-specific model to map the residual limb kinematics of the amputees to a desired normative trajectory of ankle angle and moment derived from a group of ablebodied individuals
Summary
Lower limb amputation hinders the quality of life. The passive replacements of a missing limb are incapable of restoring normal gait (Varol and Goldfarb, 2007; Windrich et al, 2016). A machine learning regression model could be employed to learn an input-output relationship between the coordinated limb motion in human gait (Boudali et al, 2017) and predict the motion of a particular limb/joint given the motion of other limbs/joints. Such a model could be potentially used as a controller for an intelligent prosthesis which aims to restore the functioning similar to an intact biological joint
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.