A combination of surface electromyography (EMG) and pattern recognition algorithms have led to improvements in the functionality of upper limb prosthetics. This method of control relies on user's ability to repeatedly generate consistent muscle contractions. Research in EMG based control of prosthesis has mainly utilized adult subjects who have fully developed neuromuscular control. Little is known about children's ability to generate consistent EMG signals necessary to control artificial limbs with multiple degrees of freedom. To address this gap, two experiments were designed to validate and benchmark an experimental protocol that quantifies the ability to coordinate forearm muscle contractions in able-bodied children across adolescent ages. Able-bodied, healthy adults (n = 8) and children (n = 9) participated in the first experiment that aimed to measure the subject's ability to produce distinguishable EMG signals. Each subject performed 8 repetitions of 16 different hand/wrist movements. We quantify the number of movement types that can be classified by Support Vector Machine with > 90% accuracy. Additional adults (n=8) and children (n=12) were recruited for the second experiment which measured the subjects' ability to control the position of a virtual cursor on a 1-DoF slide using proportional EMG control under three different gain levels. We demonstrated that children had a smaller number of highly independent movements than adults did, due to higher variability. Furthermore, we found that children had higher failure rates and slower average target acquisitions due to increased time-to-target and follow-up correction time. We also found significant correlation between forearm circumference/age and performance. The results of this study provide novel insights into the technical and empirical basis to better understand neuromuscular development in pediatric upper-limb amputees.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1684 |
| Date | 01 January 2021 |
| Creators | Gonzalez, Miguel |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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