Browsing by Subject "amputee"
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Item A pilot case series for concurrent validation of inertial measurement units to motion capture in individuals who use unilateral lower-limb prostheses(Sage Publications, 2023-07-13) Finco, M. G.; Patterson, Rita M.; Moudy, Sarah C.INTRODUCTION: Inertial measurement units (IMUs) may be viable options to collect gait data in clinics. This study compared IMU to motion capture data in individuals who use unilateral lower-limb prostheses. METHODS: Participants walked with lower-body IMUs and reflective markers in a motion analysis space. Sagittal plane hip, knee, and ankle waveforms were extracted for the entire gait cycle. Discrete points of peak flexion, peak extension, and range of motion were extracted from the waveforms. Stance times were also extracted to assess the IMU software's accuracy at detecting gait events. IMU and motion capture-derived data were compared using absolute differences and root mean square error (RMSE). RESULTS: Five individuals (n = 3 transtibial; n = 2 transfemoral) participated. IMU prosthetic limb data was similar to motion capture (RMSE: waveformItem Study of Kinematics and Gait in Dynamic Response Feet across Functional K-Level Categories.(2016-12-01) Donevant, Russell J.; Patterson, Rita; Bugnariu, Nicoleta; Rosales, ArmandoIn the United States, the Medicare Functional Classification Level (MFCL or K-level) classification system exists in order to estimate a patient’s rehabilitation potential. Physicians assign a K-level rating from 0-4 of increasing functionality, which serves to designate what kind of prosthetic device to provide a patient with and what insurance will cover. This study aims to interpret kinematic data recorded from transtibial amputees with two different functional levels of prosthetic feet and interpret the effect on gait and functional performance when switching to a higher/lower prosthetic level than the one currently equipped with. Kinematic data are collected via motion-capture and force-plate technologies while subjects interact with a virtual reality environment and processed using the GOAT (Gait Offline Analysis Tool) analysis software.