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Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks.
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This content will become publicly available on June 3, 2025
A Multimodal Assistive-Robotic-Arm Control System to Increase Independence After Tetraplegia
Following tetraplegia, independence for completing essential daily tasks, such as opening doors and eating, significantly declines. Assistive robotic manipulators (ARMs) could restore independence, but typically input devices for these manipulators require functional use of the hands. We created and validated a hands-free multimodal input system for controlling an ARM in virtual reality using combinations of a gyroscope, eye-tracking, and heterologous surface electromyography (sEMG). These input modalities are mapped to ARM functions based on the user’s preferences and to maximize the utility of their residual volitional capabilities following tetraplegia. The two participants in this study with tetraplegia preferred to use the control mapping with sEMG button functions and disliked winking commands. Non-disabled participants were more varied in their preferences and performance, further suggesting that customizability is an advantageous component of the control system. Replacing buttons from a traditional handheld controller with sEMG did not substantively reduce performance. The system provided adequate control to all participants to complete functional tasks in virtual reality such as opening door handles, turning stove dials, eating, and drinking, all of which enable independence and improved quality of life for these individuals.
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- Award ID(s):
- 1901492
- PAR ID:
- 10516547
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Volume:
- 32
- ISSN:
- 1534-4320
- Page Range / eLocation ID:
- 2124 to 2133
- Subject(s) / Keyword(s):
- Assistive robotic technology electromyography spinal cord injury usability study
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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