Name
Abstract | ||
|---|---|---|
With the increase in the aging population and people with disabilities, caused by heredity, disease, or injury, the demand for assistive devices is growing. Considerable research has been devoted in recent times on developing assistive technologies based on mobile robots to create what is now known as “smart wheelchairs.” One of the main challenges is how the user interfaces with the smart wheelchair for its smooth control. These smart wheelchairs typically consist of a powered commodity wheelchair, a computer, and a network of sensors. The common interface of a joy stick is problematic because it requires certain motor skills, which may be lacking in elderly and in people with disabilities. We have been developing a new smart wheelchair that comprises of a standard power wheelchair as the testbed, a joystick interface for manual control, an on-board computer, a laser scanner, and an EMG signal reading device. Its goal is to offer the elderly and the physically handicapped, concentrating on the portion who have sufficient motor control skills in their forearm, another way to control a power wheelchair without the means of a joystick. This paper focuses on efficiently using electromyography (EMG) to measure and utilize muscle signals for control of the wheelchair. The proposed system aims to help the user navigate within their environment, indoor or outdoor, as well as provide a capable HMI. The paper focuses on the growth of a user-friendly HCI in its application of making customizable mobilization for users on any commercial powered wheelchair. The simulation results show the potential of the system's capabilities to recognize EMG-based signals for better mobility in an indoor, open office environment. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDIS50059.2020.00011 | 2020 3rd International Conference on Data Intelligence and Security (ICDIS) |
Keywords | DocType | ISBN |
assistive technology,smart wheelchairs,human-computer interface,sensors,multimodal interface,EMG,Myo Gesture Armband,ROS,k-nearest neighbor,Euclidean distance | Conference | 978-1-7281-9380-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zainab Alibhai | 1 | 0 | 0.34 |
| Taylor Burreson | 2 | 0 | 0.34 |
| Matthew Stiller | 3 | 0 | 0.34 |
| Ishfaq Ahmad | 4 | 2884 | 192.17 |
| Manfred Huber | 5 | 66 | 5.86 |
| Addison Clark | 6 | 0 | 1.35 |