Lower-extremity amputees face numerous challenges when returning to daily activities. Amongst these challenges is the ability to safely and dynamically transition from one locomotor state to another. Switching between level-ground, ramp, and stair locomotion poses an increased risk as lower-extremity functionality is compromised. Powered prosthetics have been proposed as a solution to this problem. Hypothetically, powered prosthetics would be able to return full functional to the amputated limb. The most common and successful source of information used in algorithms for lower-extremity prosthetics has been electromyography. However, in practice, amputees remain unable to easily actuate the mechanized joints of powered prostheses. Therefore, the current project aimed to identify myoelectric activation differences in lower-extremity musculature during the gait cycles preceding locomotor transition in able-bodied, trans-tibial, and trans-femoral subjects to assist efforts in developing robust classification algorithms for locomotor transitions. Analysis of electromyography was completed to determine if there were periods of activation where classification algorithms could utilize differences in myoelectric activation to appropriately control joint actuation in a subset of eight transitions that included level-ground locomotion and switching to either ramp or stair locomotion and vice versa. Ramp transitions were fundamentally similar to level-ground locomotion and elicited no differences in myoelectric activation. Stair transitions were found to alter muscle activation patterns in able-body and trans-tibial subjects. Trans-femoral subjects differentiated from able-bodied and trans-tibial subjects due to increased recruitment pattern variability. These patterns are distinct and may suggest individual learning patterns within the trans-femoral amputee population. Further investigation of these patterns may be warranted. Findings within able-bodied and trans-tibial subjects suggest common transition based differences within each respective population. Trans-tibial classification algorithms may be developed to utilize this information, using schemes that are focused on important areas during the gait cycle.
| Identifer | oai:union.ndltd.org:uoregon.edu/oai:scholarsbank.uoregon.edu:1794/20544 |
| Date | 27 October 2016 |
| Creators | Nakamura, Bryson |
| Contributors | Hahn, Michael |
| Publisher | University of Oregon |
| Source Sets | University of Oregon |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Rights | All Rights Reserved. |
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