The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: An experimental assessment

Erdoğan, Ahmetcan; Argall, Brenna

doi:10.1016/j.robot.2017.04.013

Cited by 57 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to meet user-dependent requirements in guiding behavior, usually explicit control objectives are minimized. Typically, these cost functions are task-oriented and use a set of performance indices such as the muscular effort (Hamaya et al, 2017) or the task completion time (Erdogan and Argall, 2017) to define the utility of the autonomous behavior of a system. As these control schemes commonly focus on the explicit performance of the user, user experience is notecessarily considered, which might have a strong impact on the user acceptance and long-term usage.…”

Section: Introductionmentioning

confidence: 99%

Effect of External Force on Agency in Physical Human-Machine Interaction

Endo

Fröhner

Musić

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Effect of External Force on Agency in Physical Human-Machine Interaction

Endo

Fröhner

Musić

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

“…The effects of SC have been explored in numerous fields in which the addition of a robot partner could benefit a human operator. For example, in assistive and rehabilitation robotics, researchers have explored the effects of SC on teleoperation of smart wheelchairs (Erdogan and Argall, 2017; Trieu et al, 2008) and robotic manipulators (Kim et al, 2006). Similarly, researchers have explored SC as it applies to the teleoperation of larger mobile robots and human–machine systems, such as cars (de Winter and Dodou, 2011) and aircraft (Matni and Oishi, 2008).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Therefore, any signal sent to the system originates from the human partner . We use this filter because we are motivated by assistive robotics, in which prior research has shown that there is no consensus across users on desired assistance level (Erdogan and Argall, 2017). By allowing the user a high level of control freedom, the system encourages input from the human operator and restricts how much authority is granted to the autonomous partner.…”

Section: Mbscmentioning

confidence: 99%

Data-driven Koopman operators for model-based shared control of human–machine systems

Broad

Abraham

Murphey

et al. 2020

The International Journal of Robotics Research

View full text Add to dashboard Cite

We present a data-driven shared control algorithm that can be used to improve a human operator’s control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user’s interaction are learned from observation through the use of a Koopman operator. Using the learned model, we define an optimization problem to compute the autonomous partner’s control policy. Finally, we dynamically allocate control authority to each partner based on a comparison of the user input and the autonomously generated control. We refer to this idea as model-based shared control (MbSC). We evaluate the efficacy of our approach with two human subjects studies consisting of 32 total participants (16 subjects in each study). The first study imposes a linear constraint on the modeling and autonomous policy generation algorithms. The second study explores the more general, nonlinear variant. Overall, we find that MbSC significantly improves task and control metrics when compared with a natural learning, or user only, control paradigm. Our experiments suggest that models learned via the Koopman operator generalize across users, indicating that it is not necessary to collect data from each individual user before providing assistance with MbSC. We also demonstrate the data efficiency of MbSC and, consequently, its usefulness in online learning paradigms. Finally, we find that the nonlinear variant has a greater impact on a user’s ability to successfully achieve a defined task than the linear variant.

show abstract

“…Then the action is transferred to the wheelchair controller to move it (Mougharbel et al, 2013). The system was further tested for effectiveness (Erdogan and Argall, 2017).…”

Section: Sip-and-puff Controllermentioning

confidence: 99%

Multi-Controlled Wheelchair for Upper Extremities Disability

Riman¹

2018

Journal of Mechatronics and Robotics

View full text Add to dashboard Cite

People with motor disabilities are trying to be more autonomous by using an electric wheelchair with a conventional joystick control. However, a problem arises when these people are suffering from upper extremities disability such as tetraplegia. Several methods were implemented such as voice control, chin control, eye-blink, sip-and-puff, but each has its advantages and disadvantages. Furthermore, in some situations a method is preferred to others. In this paper, we suggest a methodology that incorporates multiple control methods for a wheelchair. Voice control, eye-blink and sipand-puff controllers will all be included in the same wheelchair. The user has the option to choose which one he will use whenever he wants. This helps the individual with disability to be more autonomous because he decides the method that suits him better at any particular time.

show abstract

The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: An experimental assessment

Cited by 57 publications

References 46 publications

Effect of External Force on Agency in Physical Human-Machine Interaction

Effect of External Force on Agency in Physical Human-Machine Interaction

Data-driven Koopman operators for model-based shared control of human–machine systems

Multi-Controlled Wheelchair for Upper Extremities Disability

Contact Info

Product

Resources

About