Search for: All records

We introduce a new design method to tailor the physical structure of a powered ankle-foot orthosis to the wearer’s leg morphology and improve fit. We present a digital modeling and fabrication workflow that combines scan-based design, parametric configurable modeling, and additive manufacturing (AM) to enable the efficient creation of personalized ankle-foot orthoses with minimal lead-time and explicit inputs. The workflow consists of an initial one-time generic modeling step to generate a parameterized design that can be rapidly configured to customizable shapes and sizes using a design table. This step is then followed by a wearer-specific personalization step that consists of performing a 3D scan of the wearer’s leg, extracting key parameters of the wearer’s leg morphology, generating a personalized design using the configurable parametric design, and digital fabrication of the individualized ankle-foot orthosis using additive manufacturing. The paper builds upon the design of the Stevens Ankle-Foot Electromechanical (SAFE) orthosis presented in prior work and introduces a new, individualized structural design (SAFE II orthosis) that is modeled and fabricated using the presented digital workflow. The workflow is demonstrated by designing a personalized ankle-foot orthosis for an individual based on 3D scan data and printing a personalized design to perform preliminary fit testing. Implications of the presented methodology for the design and fabrication of future personalized powered orthoses are discussed, along with avenues for future work. 

The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training.