Saluti dall'Italia!
It has been nearly a year since the news section of this blog was updated, and a lot has happened. Several months of hectic data collections and analyses took over the end of 2006, only to be followed by several more months of thesis writing. Luckily, in the end I did complete all the requirements for my Master's degree, and am now a University of Delaware graduate.
Also, following a strenous summer of burocratic paper work and trips to the Italian consulate in Philadelphia, I am finally in Perugia for my Research Fellowship with Let People Move. It has been good to be here, and I am excited to see where the research can go. The staff that will be helping with the research are great to work with, and can't wait to get their hands dirty with me, so that has been very encouraging, and a great start to my time over here!
Thursday, August 23, 2007
CBER Day 2007
PREDICTING MUSCLE FORCES AND JOINT MOMENTS USING SINGLE JOINT AND MULTI JOINT EMG-DRIVEN MODELS
Daniel N. Bassett, Qi Shao, Kurt T. Manal, Thomas S. Buchanan
Center for Biomedical Engineering Research, University of Delaware, Newark, DE
INTRODUCTION: Single-joint models may be adequate for some applications; however, it may be more appropriate to use a multi-joint model when studying complex motions. The present study investigates biarticular muscles in EMG-driven models accounting for their contributions to both joints they span.
METHODS: Six subjects performed normal walking, hopping, and hop-and-stop tasks while EMG, ground reaction forces, and motion data were collected. Three hybrid EMG-driven models were developed: single ankle, single knee, and multi-joint of the ankle and knee. An optimization algorithm was used to calibrate the forward dynamic Hill-type models by using the inverse dynamic joint moment as a benchmark.
RESULTS AND DISCUSSION: Normal walking comparison between forward dynamics and inverse dynamics joint moments at the ankle gave R2 values of 0.97 and 0.96 and RMS-error of 18.6% and 19.8%; whereas at the knee the R2 values were 0.80 and 0.79 and RMS-error of 20.9% and 23.1% for single-joint and multi-joint models respectively. New task predictions displayed the versatility of the calibrations for hopping ankle and hop-and-stop knee predictions which performed very similarly to walking, and compared to normal walking have similar kinematics and muscle activations. Muscle force predictions showed small variations between single and multi-joint models for the quadriceps, hamstrings, or the dorsiflexor. However, as expected, the gastrocnemii muscle forces varied significantly between the two types of models. Furthermore, a correlation was noted between the magnitude of the late stance knee flexion moment and relative magnitude of the gastrocnemii forces.
CONCLUSION: The three models perfomed very similarly for all subjects and all tasks; however, significant differences were found in the gastrocnemii force predictions. Implying single-joint models of the ankle should account for kinetics of the knee to replicate the presumably more realistic multi-joint force predictions.
Daniel N. Bassett, Qi Shao, Kurt T. Manal, Thomas S. Buchanan
Center for Biomedical Engineering Research, University of Delaware, Newark, DE
INTRODUCTION: Single-joint models may be adequate for some applications; however, it may be more appropriate to use a multi-joint model when studying complex motions. The present study investigates biarticular muscles in EMG-driven models accounting for their contributions to both joints they span.
METHODS: Six subjects performed normal walking, hopping, and hop-and-stop tasks while EMG, ground reaction forces, and motion data were collected. Three hybrid EMG-driven models were developed: single ankle, single knee, and multi-joint of the ankle and knee. An optimization algorithm was used to calibrate the forward dynamic Hill-type models by using the inverse dynamic joint moment as a benchmark.
RESULTS AND DISCUSSION: Normal walking comparison between forward dynamics and inverse dynamics joint moments at the ankle gave R2 values of 0.97 and 0.96 and RMS-error of 18.6% and 19.8%; whereas at the knee the R2 values were 0.80 and 0.79 and RMS-error of 20.9% and 23.1% for single-joint and multi-joint models respectively. New task predictions displayed the versatility of the calibrations for hopping ankle and hop-and-stop knee predictions which performed very similarly to walking, and compared to normal walking have similar kinematics and muscle activations. Muscle force predictions showed small variations between single and multi-joint models for the quadriceps, hamstrings, or the dorsiflexor. However, as expected, the gastrocnemii muscle forces varied significantly between the two types of models. Furthermore, a correlation was noted between the magnitude of the late stance knee flexion moment and relative magnitude of the gastrocnemii forces.
CONCLUSION: The three models perfomed very similarly for all subjects and all tasks; however, significant differences were found in the gastrocnemii force predictions. Implying single-joint models of the ankle should account for kinetics of the knee to replicate the presumably more realistic multi-joint force predictions.
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