publications

2024

1. Toward a Unified Naming Scheme for Thermo-Active Soft Actuators: A Review of Materials, Working Principles, and Applications

   Trevor Exley, Emilly Hays, Daniel Johnson, Arian Moridani, Ramya Motati, and Amir Jafari

   Robotics Reports, Jan 2024

   Abs

   Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as “intelligent,” “smart,” and “adaptive” materials, which add vagueness to the broad innovation among literature. Therefore, a clear, functional, and descriptive naming scheme is proposed in which a previously vague name—Soft Material for Soft Actuators—can remain clear and concise—Phase-Change Elastomers for Artificial Muscles. By synthesizing the working principle, material, and application into a naming scheme, the searchability of soft robotics can be enhanced and applied to other fields. The field of thermo-active soft actuators spans multiple domains and requires added clarity. Thermo-active actuators have potential for a variety of applications spanning virtual reality haptics to assistive devices. This review offers a comprehensive guide to selecting the type of thermo-active actuator when one has an application in mind. In addition, it discusses future directions and improvements that are necessary for implementation.

2023

1. Toward a Novel Thermal-Based Variable Impedance Module Through Adjusting Viscoelastic Properties of a Thermoresponsive Polymer

   Trevor Exley, Daniel Johnson, and Amir Jafari

   IEEE Transactions on Medical Robotics and Bionics, Nov 2023

   Abs

   In this article, we propose a one-of-a-kind thermal-based variable impedance module to be used towards developing a variable impedance actuator. Unlike other variable impedance actuators, where the impedance adjustment modules employ a mechanism to regulate their impedance, this novel module does it through controlling temperature of a thermoplastic polymer Polycaprolactone. The viscoelastic properties of polycaprolactone are temperature-dependent, increasing the rigidity when it is cooling down and softening when it is heating up. To change the temperature, thermo-electric Peltiers are embedded into the design. Preliminary experiments have shown that this module is able to adjust its impedance through changing the temperature. The simplicity and lightness of the proposed off-line impedance adjustment approach make it a suitable choice when the actuator’s size, weight, and compactness are the main concerns. This is because the proposed design is highly scalable, and by scaling down the size of the module, its performance regarding the speed of impedance adjustment would increase.

2. A Novel Variable Impedance Actuator Utilizing Adjustable Viscoelastic Properties of Thermoresponsive \emphPolycaprolactone

   Trevor Exley, Daniel Johnson, and Amir Jafari

   Robotics Reports, Nov 2023
3. Utilizing the Peltier Effect for Actuation of Thermo-Active Soft Robots

   Trevor Exley, Daniel Johnson, and Amir Jafari

   Smart Materials and Structures, Jul 2023

   Abs

   The field of soft actuation methods in robotics is rapidly advancing and holds promise for physical interactions between humans and robots due to the adaptability of materials and compliant structures. Among these methods, thermally-responsive soft actuators are particularly unique, ensuring portability as they do not require stationary pumps, or high voltage sources, or remote magnetic field. However, since working principles of these actuators are based on Joule heating, the systems are inefficient and dramatically slow, especially due to their passive cooling process. This paper proposes using the Peltier effect as a reversible heating/cooling mechanism for thermo-active soft actuators to enable faster deformations, more efficient heat transfer, and active cooling. The proposed actuator is composed of a thin elastic membrane filled with phase-change fluid that can vaporize when heated to produce large deformations. This membrane is placed in a braided mesh to create a McKibben muscle that can lift 5 N after 60 s of heating, and is further formed into a gripper capable of manipulating objects within the environment. The effectiveness of the proposed actuator is demonstrated, and its potential applications in various fields are discussed.

4. A Review of Rehabilitative and Assistive Technologies for Upper-Body Exoskeletal Devices

   Emilly Hays, Jack Slayton, Gary Tejeda-Godinez, Emily Carney, Kobe Cruz, Trevor Exley, and Amir Jafari

   Actuators, Apr 2023

   Abs

   This journal review article focuses on the use of assistive and rehabilitative exoskeletons as a new opportunity for individuals with diminished mobility. The article aims to identify gaps and inconsistencies in state-of-the-art assistive and rehabilitative devices, with the overall goal of promoting innovation and improvement in this field. The literature review explores the mechanisms, actuators, and sensing procedures employed in each application, specifically focusing on passive shoulder supports and active soft robotic actuator gloves. Passive shoulder supports are an excellent option for bearing heavy loads, as they enable the load to be evenly distributed across the shoulder joint. This, in turn, reduces stress and strain around the surrounding muscles. On the other hand, the active soft robotic actuator glove is well suited for providing support and assistance by mimicking the characteristics of human muscle. This review reveals that these devices improve the overall standard of living for those who experience various impairments but also encounter limitations requiring redress. Overall, this article serves as a valuable resource for individuals working in the field of assistive and rehabilitative exoskeletons, providing insight into the state of the art and potential areas for improvement.

2022

1. Increasing Robustness and Output Performance of Variable Stiffness Actuators in Periodic Motions

   Trevor Exley, and Amir Jafari

   Mechanism and Machine Theory, Mar 2022

   Abs

   Variable Stiffness Actuators (VSAs) have been developed to address the safety and limited adaptability in interactions with uncertainties and energy efficiency issues which exist in traditional “stiff” robots. When desired performance of a VSA is given for a certain application, the question is how this desired performance can be achieved with minimum energy consumption and maximum robustness against uncertainties. This will lead to more compact, lighter but more powerful VSAs. This work develops an understanding of how to optimally design the parameters of the stiffness adjustment mechanisms by developing a framework that can robustly maximize the output performance of VSAs. Five VSA examples, each representing a different design set of stiffness adjustment mechanism, are being considered and evaluated based on the proposed optimization framework to perform a given periodic motion. The resultant optimal design of each set is then compared with the original design in terms of output performance and robustness. The proposed framework shows improvement of the output performance for the given periodic motion up to 546%, with robustness of up to 2.1% perturbation of the optimal design values.

2. Predicting UPDRS Motor Symptoms in Individuals With Parkinson’s Disease From Force Plates Using Machine Learning

   Trevor Exley, Sarah Moudy, Rita M. Patterson, Joonghyun Kim, and Mark V. Albert

   IEEE Journal of Biomedical and Health Informatics, Jul 2022

   Abs

   Parkinson’s disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinson’s Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold. First, to evaluate quiet standing as an additional, out-of-clinic, objective feature to predict UPDRS-III subscores related to motor symptom severity; and second, to use quiet standing to detect the presence of motor symptoms. Force plate data were collected from 42 PD patients and 43 healthy controls during quiet standing (a task involving standing still with eyes open and closed) as a feasible task in clinics. Predicting each subscore of the UPDRS-III could aid in identifying progression of PD and provide specialists additional tools to make an informed diagnosis. Random Forest feature importance indicated that features correlated with range of center of pressure (i.e., the medial-lateral and anterior-posterior sway) were most useful in the prediction of the top PD prediction subscores of postural stability (r = 0.599; p = 0.014), hand tremor of the left hand (r = 0.650; p = 0.015), and tremor at rest of the left upper extremity (r = 0.703; p = 0.016). Quiet standing can detect body bradykinesia (AUC-ROC = 0.924) and postural stability (AUC-ROC = 0.967) with high predictability. Although there are limited data, these results should be used as a feasibility study that evaluates the predictability of individual UPDRS-III subscores using quiet standing data.

3. Predicting GPU Performance and System Parameter Configuration Using Machine Learning

   Zhuren Liu, Trevor Exley, Austin Meek, Rachel Yang, Hui Zhao, and Mark V. Albert

   In 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Jul 2022

   Abs

   GPUs are widely used in accelerating computation-intensive applications. Performance models are important for designing high-performance and cost-efficient GPUs. In this work, we developed machine learning models that can accurately predict GPU system performance. Our model can identify important features that can provide insights to designers on the most important hardware system parameters when executing their applications. We also developed a model for predicting minimum system configuration parameters based on performance. Our model can provide system configuration recommendations for users to meet their performance requirements.

2021

1. Maximizing Energy Efficiency of Variable Stiffness Actuators through an Interval-Based Optimization Framework

   Trevor Exley, and Amir Jafari

   Sens. Actuators A Phys., Dec 2021

   Abs

   Despite the necessity for being able to regulate the stiffness of the robotic platforms in physical contact with humans, and tremendous number of different variable stiffness actuators that have been developed so far, there is still no such actuator that has successfully passed the research lab phase and transferred into a real application. The main reason is due to the lack of understating on how to optimally design a stiffness adjustment mechanism based on desired performances of each application. If not optimally designed, the additional complexities within the actuators, prevent to win the trade-off between benefits of having a stiffness adjustment mechanisms versus its costs, such as the energy storage capacity of the elastic elements compared to how much energy can be actually released at the output of the actuator, i.e. the link. Currently, this trade-off criterion is not in favor of introducing a stiffness adjustment mechanism into the actuator. Therefor, generally, having a simple series elastic actuator with active compliance has been preferred over having a complex variable stiffness actuator, in many real applications. This work develops an understanding of how to optimally design the parameters of a stiffness adjustment mechanism by developing a framework that can robustly maximize the energy efficiency of variable stiffness actuators. Five different design sets of stiffness adjustment mechanism are being considered and evaluated based on the proposed optimization framework. The resultant optimal design of each set is then compared with the original design in terms of energy efficiency. The proposed framework shows improvement of energy efficiency up to 354%, while design constraints are all being satisfied.