Fey, Ann Majewicz

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6509

Ann Majewicz Fey is an Assistant Professor of Mechanical Engineering. She also serves as the head of the Human-Enabled Robotic Technology (HeRo) Lab. In 2019 she received a Faculty Early Career Development (CAREER) Award from the National Science Foundation. Her professional goal is to "build safer and and more effective robots" designed to partner with human doctors in teleoperated surgeries. Centered around this goal her research interests include:

  • Robotics
  • Dynamic systems and control
  • Medical and surgical robots
  • Teleoperation and
  • Haptics

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    Haptic Stroke Testbed for Pharmacological Evaluation of Dynamic Allodynia in Mouse Models
    (IEEE Computer Society) Lee, Jin; Atwood, Brian J.; Megat, Salim; Dussor, Gregory; Price, Theodore J.; Fey, Ann Majewicz; Lee, Jin; Atwood, Brian J.; Megat, Salim; Dussor, Gregory; Price, Theodore J.; Fey, Ann Majewicz
    Dynamic mechanical allodynia is an aggravating neuropathological condition in which light, physical touch leads to pain. Developing pharmaceutical agents to treat this condition requires extensive animal trials using a mouse model, and a laborious process of manually stroking inflicted mouse paws, with a brush or cotton swab, while recording responses to that stimulus. In this paper, we developed an autonomous testing mechanism to create repeatable stroking sensations for mice during dynamic allodynia testing. The chamber consists of a belt driven brush mechanism and light and dark chambers. Additionally, we conducted a human subjects study to determine the baseline variability in human-performed dynamic allodynia testing. Our tactile stoke display is capable of stroking a mouse paw between 1-5 mm/s with a repeatable force. In our human subject experiments, the user applied force ranged from 0.1-9.0 gF with a maximum standard deviation of 4.13 gF. In contrast, our device is capable of producing repeatable brush strokes at 0.69 gF (SD = 0.13 gF) and 1.78 gF (SD = 0.16 gF) for two brushes. Preliminary animal studies show that normal mice are not disturbed by the stroking sensation; however, mice afflicted with allodynia move away from it. On average the injured mice spent 90% of their time in a bright, adverse environment to avoid the brush, whereas normal mice only spent 40% of their time in the bright environment.
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    Human-Centric Predictive Model of Task Difficulty for Human-In-The-Loop Control Tasks
    (Public Library of Science) Wang, Ziheng; Fey, Ann Majewicz; Wang, Ziheng; Fey, Ann Majewicz
    Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R² = 0.927), followed by a model using only kinematic metrics (R² = 0.921). Both models were better predictors of task difficulty than the movement time model (R² = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control.

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