About

Kimberly Stubbs, PhD, is an Instructional Assistant Professor in the Department of Engineering Education within the Herbert Wertheim College of Engineering at the University of Florida. She holds a Ph.D. in mechanical engineering from the University of Florida, where she also earned her master's and bachelor's degrees in mechanical engineering, along with an associate of arts degree from St. John's River State College. In her current role, she teaches and supervises undergraduate students, focusing on developing skills in the MATLAB coding environment through an experiential learning-based flipped course format. Additionally, she serves as a faculty mentor for an Integrated Product and Process Design (IPPD) team sponsored by Pinecrest Gardens, working on the development of an animatronic interactive display.

Research topics

• Computer Science
• Artificial Intelligence
• Physical medicine and rehabilitation
• Medicine
• Psychology
• Physics
• Engineering
• Physical therapy
• Neuroscience
• Simulation

Selected publications

• Evaluation of a control paradigm allowing heart rate guided rehabilitative exercise for boys with Duchenne muscular dystrophy

  Journal of NeuroEngineering and Rehabilitation · 2025-12-29 · 1 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: Aerobic cycle-training counters deconditioning and induces muscle and cardiorespiratory benefits in various neuromuscular disorders. However, its application to Duchenne muscular dystrophy (DMD) is limited due to lack of exercise prescription guidelines, particularly for intensity. A balance between beneficial versus harmful effects of muscle activity must be established given the weakness and concerns of contraction-induced damage inherent to DMD. Previous studies in DMD used motor-assisted cycling applying subjective ratings of perceived exertion to guide exercise intensity, whereas objective parameters such as heart rate (HR) or work performed were not reported. In efforts to develop exercise guidelines for DMD, we designed a motor-assisted cycle-exercise paradigm using closed-loop control of motor effort and individualization of intensity based on HR. Feasibility of this paradigm in DMD was tested in the home setting with remote clinical supervision. METHODS: A closed-loop controller was developed with user-defined saturation points for cadence and baseline motor inputs to ensure safety of cycling and adjustments in level of muscle overload (assistive current). The controller allowed remote, interactive adjustment of current based on HR biofeedback, providing cycling assistance when velocity approached a lower-bound and resistance when the upper-bound was approached. A target intensity of 40-50% HR reserve was individualized for each participant and motor effort was adjusted accordingly by the clinician. Force-sensors were embedded in the pedals for quantification of passive and active power. RESULTS: Six ambulatory boys with DMD (aged 7.7 ± 0.9 years) completed at least two bouts of cycling exercise (3-10 min per bout) with an average 0.53 ± 0.15 amps assistive current (range 0.3-0.8 amps). HR increased from rest during passive and active cycling (mean 109.2 ± 6.1; 119.2 ± 8.5; 149.7 ± 4.6 bpm respectively), where boys were actively exercising at 45% of HR reserve at an average cycling power of 5.7 ± 1.3 watts (ranging 3-8 watts depending on disease severity). CONCLUSION: These results show for the first time that boys with DMD can cycle actively to generate power and raise HR to a prescribed intensity, supporting feasibility of this home-based, remotely-supervised control paradigm. They warrant future study to establish clinical exercise prescription parameters and the potential of aerobic cycling as a rehabilitative strategy in DMD.

  PublisherOA PDFDOI

• Evaluation of a control paradigm allowing heart rate guided rehabilitative exercise  for boys with Duchenne muscular dystrophy 

  Research Square · 2025-06-10

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• WIP: Enhancing Grading Efficiency in Engineering Education Through Automation on a Flipped Classroom

  2025-11-02

  article

  This Work-in-Progress paper and Innovative Practice presents a practical method for efficient grading in engineering education by integrating MATLAB Grader with a custom Python script. The approach addresses challenges in flipped classrooms where timely, accurate assessments are critical. Because MATLAB Grader's integration with LMS platforms like Canvas is limited, the authors developed a Python tool to automate grade extraction, format data for institutional rosters, and facilitate uploads to Canvas, reducing instructor workload and improving grading accuracy. Early feedback indicates increased student satisfaction from immediate feedback and reduced grading time for instructors. This practice highlights how educators can leverage automation to enhance learning, with future work to improve usability and LMS integration.

  PublisherDOI

• Telerehabilitation Through a Remotely Operated Motorized Functional Electric Stimulation Actuated Cycle

  IEEE Transactions on Control Systems Technology · 2023-12-14

  article1st authorCorresponding

  Neuromuscular disorders (NDs) affect millions of people each year, many of whom are prescribed functional electrical stimulation (FES) rehabilitative cycling. However, it is often difficult for many with NDs to attend regularly scheduled physical therapy sessions, a fact which is exasperated by the ongoing COVID-19 pandemic. This article details the development of a teleoperated FES-actuated rehabilitation system for two use cases: a remote physical therapy session for people not able to attend in person, and a rehab-by-wire style system where the rehabilitation participant sets the desired trajectory of the FES-actuated lower-body cycle using a motorized hand-cycle, thus coordinating the upper and lower limbs. In both cases, the lower-body rehabilitation cycle has a split-crank to capture asymmetries in lower-body performance. Lyapunov-based analysis methods are used to prove global exponential tracking to the desired position and cadence determined by the master-cycle system. Five people were tested for each use case, where the teleoperated FES-actuated rehabilitative lower-body cycling system resulted in an average rms position error (calculated across both use cases) of 6.14° and an average rms cadence error of 3.77 RPM, despite an unpredictable, variable desired cadence. The calculated average position error was found to be 0.04°+/−5.96°, thus eliminating undesirable steady-state position errors reported in prior works.

  PublisherDOI

• Robust cadence tracking for switched FES-cycling using a time-varying estimate of the electromechanical delay

  Automatica · 2022-07-18 · 6 citations

  article
  PublisherDOI

• Model-Based Switched Approximate Dynamic Programming for Functional Electrical Stimulation Cycling

  2022 American Control Conference (ACC) · 2022-06-08 · 1 citations

  article

  This paper applies a reinforcement learning-based approximately optimal controller to a motorized functional electrical stimulation-induced cycling system to track a desired cadence. Sufficient torque to achieve the cycling objective is achieved by switching between the quadriceps muscle and electric motor. Uniformly ultimately bounded (UUB) convergence of the actual cadence to a neighborhood of the desired cadence and of the approximate control policy to a neighborhood of the optimal control policy are proven for both motor control and muscle control via a Lyapunov-based stability analysis provided developed dwell-time conditions that determine when to switch between the motor or the muscle are satisfied. Lyapunov-based techniques are also used to derive a minimum dwell-time condition to prove UUB stability of the overall switched system.

  PublisherDOI

• Robust Cadence and Power Tracking on a Switched FES Cycle With an Unknown Electromechanical Delay

  IEEE Transactions on Control Systems Technology · 2022-05-24 · 3 citations

  article

  Functional electrical stimulation (FES) is commonly used to facilitate cycling tasks for people with lower-limb movement disorders. In this work, FES and motor controllers are designed to track a desired power and cadence, respectively, and a Lyapunov-based switched systems analysis is performed to guarantee uniformly ultimately bounded power tracking and global exponential cadence tracking for a switched, delayed, nonlinear, and uncertain FES-cycling system. A unique challenge in this problem is that there is an unknown time-varying input delay to produce force, and a different unknown time-varying residual input delay where force is still produced after stimulation is removed. These delays impact the dwell-time conditions that dictate stimulation timing, and if not properly accounted for can lead to undesired effects such as antagonistic muscles exerting force at the same time or potential instabilities. The proposed controllers were validated by experimental analysis of four participants with neurological conditions (NCs) and five able-bodied participants, and yielded average power and cadence tracking errors of 0.01 ± 0.09 W and −0.05 ± 0.65 revolutions per minute (RPM), respectively, for the able-bodied participants and 0.01 ± 1.11 W and −0.07 ± 1.17 RPM, respectively, for the participants with NCs.

  PublisherDOI

• Position and cadence tracking of a motorized FES-cycle with an unknown time-varying input delay using saturated FES control

  Automatica · 2022 · 8 citations

  • Computer Science
  • Physical medicine and rehabilitation
  • Artificial Intelligence
  PublisherDOI

• Exponential Stability with RISE Controllers for Uncertain Nonlinear Systems with Unknown Time-Varying State Delays

  2022 IEEE 61st Conference on Decision and Control (CDC) · 2022-12-06 · 7 citations

  article

  Robust Integral of the Sign of the Error (RISE) controllers have gained popularity in recent years due to their ability to achieve asymptotic tracking error convergence using a continuous control input for uncertain nonlinear systems. In a recent breakthrough, it was shown that the tracking error convergence with RISE controllers is also exponential, whereas previously it was thought to be only asymptotic. However, it remains an open question whether this exponential stability result also holds for systems containing unknown time-varying state delays. In this paper, a novel strict candidate Lyapunov function is developed to prove exponential stability with a RISE controller for uncertain nonlinear systems involving unknown time-varying state delays. Additionally, a simulation example is provided to demonstrate the performance of a RISE controller in the presence of unknown time-varying state delays. The results indicate a higher sensitivity of tracking error and control effort to the delay frequency as compared to the delay magnitude.

  PublisherDOI

• Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2022 · 12 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Hybrid exoskeletons, which combine functional electrical stimulation (FES) with a motorized testbed, can potentially improve the rehabilitation of people with movement disorders. However, hybrid exoskeletons have inherently nonlinear and uncertain dynamics, including combinations of discrete modes that switch between different continuous dynamic subsystems, which complicate closed-loop control. A particular complication is the uncertain muscle control effectiveness associated with FES. In this work, adaptive integral concurrent learning (ICL) motor and FES controllers are developed for a hybrid biceps curl exoskeleton, which are designed to achieve opportunistic and data-based learning of the uncertain human and electromechanical testbed parameters. Global exponential trajectory tracking and parameter estimation errors are proven through a Lyapunov-based stability analysis. The motor effectiveness is assumed to be unknown, and, to help with fatigue reduction, FES is enabled to switch between multiple electrodes on the biceps brachii, further complicating the analysis. A consequence of switching between the different uncertain subsystems is that the parameters must be opportunistically learned for each subsystem (i.e. each electrode and the motor), while that subsystem is active. Experiments were performed to validate the developed ICL controllers on twelve healthy participants. The average (± standard deviation) position tracking errors across each participant were 1.44 ± 5.32 deg, -0.25 ± 2.85 deg, and -0.17 ± 2.66 deg across biceps Curls 1-3, 4-7, and 8-10, respectively, where the average across the entire experiment was 0.28 ± 3.53 deg.

  PublisherOA PDFDOI

Frequent coauthors

• Warren E. Dixon

  University of Florida

  18 shared

• Brendon C. Allen

  13 shared

• Emily J. Griffis

  University of Florida

  2 shared

• Hannah M. Sweatland

  University of Florida

  1 shared

• Duc M. Le

  Aurora Flight Sciences (United States)

  1 shared

• Andrés M. Rubiano

  Universidad El Bosque

  1 shared

• Noel A. Thomas

  1 shared

• Lauren E. Rogers

  1 shared

Education

• Doctor of Philosophy in Mechanical Engineering, Mechanical and Aerospace Engineering

  University of Florida

• Master of Science in Mechanical Engineering, Mechanical and Aerospace Engineering

  University of Florida

  2021

• Bachelor of Science in Mechanical Engineering, Mechanical and Aerospace Engineering

  University of Florida

  2019