UNB Scholar Research Repository :: Browsing by Author "Sensinger, Jonathon"

Browsing by Author "Sensinger, Jonathon"

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A dynamic approach to balance assessment using self-induced perturbations
(University of New Brunswick, 2020) Chambers, Neil Cameron; Scheme, Erik; Sensinger, Jonathon
A novel balance assessment method for older adults was developed using a balance platform that induces dynamic self-perturbations and which could use synchronization as an adjustable level of assistance. Rather than measure the subject’s performance, the proposed balance assessment measures how much assistance they need to reach a standard level of performance. A swaying balance platform was constructed and instrumented to conduct the balance assessment with different levels of assistance. Nineteen healthy young adults were tested with self-perturbations introduced by following swaying visual instructions that changed frequency instantaneously. Experimental results were unable to confirm if synchronization was capable of providing assistance, nor whether the assessment outcomes could benefit from applying different levels of assistance. Future studies should focus on understanding how to ensure that synchronization occurs in the combined subject/instructions system.
Autonomous assistance-as-needed control of a lower limb exoskeleton with guaranteed stability
(University of New Brunswick, 2020) Campbell, Samuel; Sensinger, Jonathon; Diduch, Chris
Lower-limb stroke rehabilitation is physically demanding on therapists and requires the concerted effort of multiple staff members. Researchers have accordingly begun investigating the use of lower-limb exoskeletons for rehabilitation. Unfortunately, if the exoskeleton ensures the correct trajectory regardless of whether or not the user contributes effort, rehabilitation can be ineffective as the patient can begin to slack. Recent research suggests using assistance-as-needed control to facilitate functional motor recovery by only applying torques if the patient deviates too far from the desired trajectory. Assistance-as-needed control has been difficult to employ in lower-limb exoskeletons, however, due to the need to ensure stability. This work demonstrates how virtual constraint control—a method used in prostheses and assistive exoskeleton control with robust stability properties—can be combined with a velocity-modulated deadzone to ensure stability. The simulations suggest the method can accommodate a large deadzone while remaining stable across a range of gait pathologies.
Control architecture for biped robots based on contraction analysis
(University of New Brunswick, 2018) Bautista-Quintero, Ricardo; Carretero, Juan; Dubay, Rickey; Sensinger, Jonathon
In recent years, the field of robotics has played a major role in the quest for restoring mobility to patients who suffered a limb impairment. Particularly, the literature in the field of bipedal robotics has provided scientific support for understanding the biomechanical interaction between artificial lower-limbs and disabled people. However, after several decades of technological progress in actuators and sensors of biped robots, there is still not enough understanding how to mimic the dexterity and efficiency of human bipedal locomotion. There is a clear open problem in the feedback control field that poses important challenges for decoding and reconstructing the fundamental biological behaviour embedded in nature. In this context, the thesis introduces a novel control architecture that is based on contraction theory and synchronisation. Combining decentralized multiple nonlinear controllers (synchronised by a virtual dynamic system) creates a mathematical abstraction of the human locomotion control. This thesis is meant to prove stability of a robust controller for an anthropomorphic walking robot. This approach is designed to minimise the risk of falling during hard joint constraints. The outcome of this research is intended to support control technology of walking assistants (e.g., exoskeletons) for patients with abnormal motor pathology (e.g., spasticity disorder due to post-stroke condition). Both theoretical and validation work presented in this thesis outperformed the results expected.
Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator
(Public Library of Science, 2018-12) Blustein, Daniel; Shehata, Ahmed; Englehart, Kevin; Sensinger, Jonathon; Maurice A. Smith
Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person’s response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person’s perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person’s learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.
Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?
(Public Library of Science, 2015) Sensinger, Jonathon; Aleman-Zapata, Adrian; Englehart, Kevin; Shu-Dong Zhang
Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which for the sake of mathematical tractability is typically represented as a series of quadratic terms. Recent studies have found that people actually use cost functions for reaching tasks that are slightly different than a quadratic function, but it is unclear which of several cost functions best explain human behavior and if these cost functions generalize across tasks of similar nature but different scale. In this study, we used an inverse-decision-theory technique to reconstruct the cost function from empirical data collected on 24 able-bodied subjects controlling a myoelectric interface. Compared with previous studies, this experimental paradigm involved a different control source (myoelectric control, which has inherently large multiplicative noise), a different control interface (control signal was mapped to cursor velocity), and a different task (the tracking position dynamically moved on the screen throughout each trial). Several cost functions, including a linear-quadratic; an inverted Gaussian, and a power function, accurately described the behavior of subjects throughout this experiment better than a quadratic cost function or other explored candidate cost functions (p<0.05). Importantly, despite the differences in the experimental paradigm and a substantially larger scale of error, we found only one candidate cost function whose parameter was consistent with the previous studies: a power function (cost ∝ errorα) with a parameter value of α = 1.69 (1.53–1.78 interquartile range). This result suggests that a power-function is a representative function of user’s error cost over a range of noise amplitudes for pointing and tracking tasks.
Improving performance and internal model strength of myoelectric prosthesis control strategies using augmented feedback
(University of New Brunswick, 2018) Shehata, Ahmed, Wagdy Emam; Sensinger, Jonathon; Scheme, Erik
The ability to reach, grasp, and lift requires reliable control, a strong understanding of the arm properties, and some feedback to achieve quick and precise movements. These normal everyday activities present a challenge for upper limb amputees. Myoelectric battery-powered prostheses have been used as one approach to tackle this challenge. Myoelectric signals are highly variable signals produced by muscle contractions that require processing before being used to control prostheses' movements. For multiple degrees of freedom motion, myoelectric controllers are either robust but provide inadequate feedback, or noisy but provide rich feedback. Feedback affects both control and the development of internal models, which in turn affects the overall performance of the prostheses. The human brain has an internal model built for the arm, which imitates its behavior, predicts consequences of an action, and computes an action based on desired consequences. Researchers have been so far unable to decouple the feedback from the control, which has forced them to develop control strategies that might enable strong control signals, but at the expense of internal model strength. The main objective of this work is to effectively decouple feedback from control by using augmented feedback and subsequently independently optimize both control and the internal model. In this work, a novel augmented-feedback myoelectric control strategy is introduced and assessed using psychophysical tests and commonly used performance measures. Results show that this developed controller enables more precise internal models, resulting in better performance than currently available controllers.
Multi-objective user priority-based optimal tuning of myoelectric prostheses
(University of New Brunswick, 2024-08) Arunachalam, Anjana Gayathri; Sensinger, Jonathon; Englehart, Kevin
Current myoelectric prosthesis control methods lack personalized tuning based on individual user preferences leading to potentially suboptimal user performance. This thesis introduces a model for personalized myoelectric prosthetic control, integrating user preferences into device dynamics through an optimization approach. Drawing on principles of computational motor control, evolutionary multi-objective optimization, and control theory, the model identifies optimal device dynamic parameters based on user preferences for effort, movement time, and reliability. Results from our simulated prosthesis model suggest that customized prosthetic devices could appreciably improve movement outcomes compared to conventional devices. This study provides a foundation for intuitive and effective prosthetic device control. These improvements may have potential applications beyond prosthesis including various human-machine interfaces.
Multi-objective user tunable interface for assistance control of a lower limb exoskeleton: a step in the right direction
(University of New Brunswick, 2020) Stewart, Kurt; Sensinger, Jonathon; Diduch, Chris
The field of assistive lower limb exoskeletons lacks controllers that allow user adjustment according to their needs and desires. This thesis develops and shows simulation evidence for allowing user-intuitive control by providing adjustment of gait based on gait performance measures the user cares about while walking. Using the NSGA-II multiobjective optimization algorithm to generate trajectories for a virtual constraint-based exoskeleton system a lookup table was generated which provides user adjustment of speed, comfort, effort proxy, and natural walking measures. The findings in this thesis demonstrate a variety of gait across these performance measures which can be used to formulate a user-adjustable controller.
The effects of simultaneous control noise in 2-degree-of-freedom tasks on optimal control strategies
(University of New Brunswick, 2018) Wilson, Katie; Englehart, Kevin; Sensinger, Jonathon
Understanding the stereotypical characteristics of human movement can better inform rehabilitation practices by providing a template of healthy and expected human motor control. Multiplicative noise is inherent in goal-directed movement, and plays an important role in computational motor control models to help support phenomena such as stereotypical kinematic profiles in time-constrained and unconstrained tasks. Most tasks are not carried out along an isolated degree-of-freedom (DOF), and modelling the contribution of noise can be difficult. In this work, we add a noise term proportional to the degree of simultaneity for multi-DOF tasks to approximate the contribution of system noise, and compare the simulation results against data from a 2-DOF experiment. With this approach, our model is able to explain previously observed motor phenomena including the presence of submovements in multi-DOF tasks, and the transition from simultaneousto sequential control of joints without the presence of visual feedback.

Browsing by Author "Sensinger, Jonathon"

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