Momona Yamagami

HCI Researcher - PhD Candidate


Headshot of a female-presenting person with round glasses and short, black hair.

I am a PhD candidate at the University of Washington in Seattle, WA, advised by Profs. Kat Steele and Sam Burden. My research focuses on modeling and enhancing human movement in the intersection of controls, rehabilitation, and accessibility in human-computer interaction (HCI). I am developing novel input techniques to promote ubiqutious rehabilitation for people with limited movement through accessible design.

My dissertation research primarily focuses on modeling and enhancing continuous device interaction for people with and without limited movement using electromyography (EMG, electrical signals from muscles) signals as an accessible and novel input technique. My future research plans include the design and development of novel input techniques that promote rehabilitation while enhancing device accessibility.

Research


Muscle Interfaces for Contiuous Device Accessibility

Electromyography, or EMG data is useful because even if a person does not have the muscle strength to move their arms, we can still pick up weak EMG signals that signify the person’s intent to move their arms. I explore EMG data as a viable alternative to a manual computer interface to control a cursor on a screen. Both users with and without limited movementpreferred and performed better with the muscle versus the manual interface for the complex continuous task. Error correction, but not underlying user intent, was altered among the users with limited movement. These results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.

Modeling Intent and Error Correction During Continuous Interactions

Deriving user intent from continuous interactions like mouse navigation or driving a car is a complicated modeling task. I use methods from control theory to model intent (feedforward control) and error correction (feedback control) for a one-dimensional trajectory-tracking task to compare learned controllers between the dominant and non-dominant hands.

Publications


2021

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking
Momona Yamagami, Lauren N. Peterson, Darrin Howell, Eatai Roth, Samuel A. Burden.
Under review in IEEE Transactions on Cybernetics | Link to draft

2020

Effects of virtual reality environments on overground walking in people with Parkinson disease and freezing of gait
Momona Yamagami, Sheri Imsdahl, Olivia Bellatin, Nawat Nhan, Samuel A Burden, Sujata Pradhan, Valerie E Kelly
Disability and Rehabilitation: Assistive Technology 2020. | Link

Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance
Momona Yamagami, Katherine M Steele, Samuel A Burden
In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2020. | Link | Video

2019

Experiments with Sensorimotor Games in Dynamic Human/Machine Interaction
Benjamin Chasnov, Momona Yamagami, Behnoosh Parsa, Lillian J Ratlif, Samuel A Burden
Micro- and Nanotechnology Sensors, Systems, and Applications XI, International Society for Optics and Photonics | Link

2018

Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements
Momona Yamagami, Keshia M Peters, Ivana Milanovic, Irene Kuang, Zeyu Yang, Nanshu Lu, Katherine M Steele
Sensors | Link

Contributions of Feedforward and Feedback Control in a Manual Trajectory-Tracking Task
Momona Yamagami, Darrin Howell, Eatai Roth, Samuel A Burden
2nd IFAC Conference on Cyber-Physical & Human Systems | Link

Teaching


Control System Analysis I

ECE 447, UW

This is a senior-level control system course focusing stability of feedback systems by root locus and frequency-response methods at the University of Washington. I have TAed this class in Fall 2019, Spring 2020 (remote) and will be teaching the course Spring 2021 (remote).