Will Flanagan

Purpose: Ankle stiffness can be altered by normal aging, bone and joint pathology, and treatments such as orthoses or surgical joint fusion. The effects of ankle stiffness on gait are not yet well understood but may be crucial for understanding how these pathologies and treatments influence body mechanics. The objective of this work was to investigate how isolated changes in stiffness applied in parallel with the ankle impact lower-limb kinematics, kinetics, joint work, and muscle activation during walking in individuals without lower-limb pathology. Methods: Nine young adults without lower-limb pathology wore an adjustable-stiffness ankle exoskeleton and walked at 31 different conditions of ankle spring stiffness, neutral angle, and treadmill incline. We recorded motion capture data, ground reaction forces, and muscle activation, and analyzed the resultant data for trends as a function of ankle stiffness. Results: Exoskeleton-side ankle range of motion decreased and asymmetry increased across all joints as ankle stiffness increased, primarily due to decreased plantarflexion at toe-off. The 30 Nm/rad spring stiffness condition led to a minimum in mean exoskeleton-side muscle activation and hip joint work, but increased kinematic asymmetry. Conclusion: Our results suggest that there may exist a range of stiffnesses at the lower end of typically-studied values that can reduce muscle activation and joint work during walking, though at the cost of kinematic symmetry. These findings provide a deeper understanding of how ankle stiffness influences gait mechanics, with potential applications in wearable devices, clinical rehabilitation, and assistive technology.

Application of force through controllable magnetic fields holds enormous potential for actuation with little-to-no physical contact in mechatronic systems. Permanent magnet arrays have gained traction as an efficient means of shaping and controlling magnet fields in mechatronic systems, creating new incentives for engineers to design, build, and test new magnet array architectures that unlock new actuation capabilities. Unfortunately, these arrays are both expensive to build and difficult to assemble. In this manuscript, we present a general approach to building complex magnet arrays using low-cost and tileable cube magnets. This method implements practical voxelization and discretization of any magnet array into tileable cube elements, such that assembly of these discrete arrays is straightforward and scalable to any geometry or array design. We illustrate this approach in developing a robot end effector that utilizes a discretized controllable axial-flux Halbach array (D-CAHA). Performance of our discretized prototype in simple, easy-to-model experiments was consistent with model predictions. Our results also enriched the model by providing important empirical insights with implications for mechatronic design. Finally, we discuss the paradigm of discrete approximation in building magnet arrays, describe its novel capabilities in the context of developing mechatronic systems, and highlight open problems in generalizable discretization.

Application of force through controllable magnetic fields holds enormous potential for actuation with little-to-no physical contact in mechatronic systems. Permanent magnet arrays have gained traction as an efficient means of shaping and controlling magnet fields in mechatronic systems, creating new incentives for engineers to design, build, and test new magnet array architectures that unlock new actuation capabilities. Unfortunately, these arrays are both expensive to build and difficult to assemble. In this article, we present a general approach to building complex magnet arrays using low-cost and tileable cube magnets. This method implements practical voxelization and discretization of any magnet array into tileable cube elements, such that assembly of these discrete arrays is straightforward and scalable to any geometry or array design. We illustrate this approach in developing a robot end effector that utilizes a discretized controllable axial-flux Halbach array. Performance of our discretized prototype in simple, easy-to-model experiments was consistent with model predictions. Our results also enriched the model by providing important empirical insights with implications for mechatronic design. Finally, we discuss the paradigm of discrete approximation in building magnet arrays, describe its novel capabilities in the context of developing mechatronic systems, and highlight open problems in generalizable discretization.

Magnetic fields enable force application without mechanical connection. By this means, electromagnets apply force across a distance, but require continuous power to maintain constant force. In contrast, switchable permanent magnet mechanisms allow unpowered constant force application, but are typically used in close-range (small gap) applications. Hence, a device enabling low-power force application over greater distances (large gap) is needed. To meet this need, we introduce an adjustable magnetic actuator called a controllable, axial-flux Halbach array (CAHA). The novelty of our design lies in the concentric nesting of two axial-flux Halbach rings. By nesting these rings, the magnetic field extends out axially on one side, with a strength dependent on the relative rotation between the rings. To demonstrate how this mechanism performs in magnetic suspension tasks, we simulated a CAHA design model, compared this design model to alternative magnetic actuators, swept geometric parameters of the design, and empirically characterized a CAHA prototype. Our simulations show that our device required much less power than an electromagnet for low-frequency (<16 Hz) tasks, with an up to 93% reduction in average power for a 5 Hz task. The CAHA model also had force densities up to 2.3x higher than a switchable magnet for large-gap (≥10 mm) applications. In empirical tests of the CAHA, magnetic performance closely matched our simulations. These results represent an introduction to the CAHA design that could improve magnetic suspension systems for low-frequency, large-gap applications.

Objective: Current socket-based methods of prosthetic limb attachment are responsible for many of the dominant problems reported by persons with amputation. In this work, we introduce a new paradigm for attachment via electromagnetic attraction between a bone-anchored ferromagnetic implant and an external electromagnet. Our objective was to develop a design framework for electromagnetic attachment, and to evaluate this framework in the context of transfemoral amputation. Methods: We first used inverse dynamics to calculate the forces required to suspend a knee-ankle-foot prosthesis during gait. We then conducted cadaveric dissections to inform implant geometry and design a surgical methodology for covering the implant. We also developed an in silico framework to investigate how electromagnet design affects system performance. Simulations were validated against benchtop testing of a custom-built electromagnet. Results: The physical electromagnet matched simulations, with a root-mean-square percentage error of 4.2% between measured and predicted forces. Using this electromagnet, we estimate that suspension of a prosthesis during gait would require 33 W of average power. After 200 and 1000 steps of simulated walking, the temperature at the skin would increase 2.3 °C and 15.4 °C relative to ambient, respectively. Conclusion: Our design framework produced an implant and electromagnet that could feasibly suspend a knee-ankle-foot prosthesis during short walking bouts. Future work will focus on optimization of this system to reduce heating during longer bouts. Significance: This work demonstrates the initial feasibility of an electromagnetic prosthetic attachment paradigm that has the potential to increase comfort and improve residual limb health for persons with amputation.

The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user's state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of level-ground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99% accuracy and ambulation mode transitions with 96% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.