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Rulik I, Sunny MSH, Sanjuan De Caro JD, Zarif MII, Brahmi B, Ahamed SI, Schultz K, Wang I, Leheng T, Longxiang JP, Rahman MH. Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks. Front Robot AI 2022; 9:885610. [PMID: 35937617 PMCID: PMC9354078 DOI: 10.3389/frobt.2022.885610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.
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Affiliation(s)
- Ivan Rulik
- Department of Computer Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
- *Correspondence: Ivan Rulik,
| | - Md Samiul Haque Sunny
- Department of Computer Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | | | | | - Brahim Brahmi
- Electrical Engineering Department, Collège Ahuntsic, Montreal, QC, Canada
| | | | - Katie Schultz
- Assistive Technology Program, Clement J. Zablocki VA Medical Center, Milwaukee, WI, United States
| | - Inga Wang
- Department of Rehabilitation Sciences & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Tony Leheng
- UFACTORY Technology Co., Ltd., Shenzhen, China
| | | | - Mohammad H. Rahman
- Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
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Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1635672. [PMID: 35720904 PMCID: PMC9200502 DOI: 10.1155/2022/1635672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/24/2022] [Accepted: 05/06/2022] [Indexed: 11/27/2022]
Abstract
The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs “Mapping Knowledge Domains” (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.
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Wu L, Alqasemi R, Dubey R. Development of Smartphone-Based Human-Robot Interfaces for Individuals With Disabilities. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3010453] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Campeau-Lecours A, Cote-Allard U, Vu DS, Routhier F, Gosselin B, Gosselin C. Intuitive Adaptive Orientation Control for Enhanced Human–Robot Interaction. IEEE T ROBOT 2019. [DOI: 10.1109/tro.2018.2885464] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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