Alternative objective function to predict reasonable muscle forces using a Hill-type muscle model
© Son et al; licensee BioMed Central Ltd. 2014
Published: 8 April 2014
Joint moments modeled from a musculoskeletal tool differ from those recorded by a dynamometer. In order to solve the problem, numerical methods to minimize the variance of the joint moments have been adopted . The existing objective function (EOF) in the optimization, however, might not be sufficient to estimate reasonable muscle forces due to a possibility of predicting well-matched joint moments with the combination of unrealistic individual muscle forces . In this study, we introduce a new objective function (NOF) for predicting reasonable muscle forces and to compare its performance with EOF.
NOF was designed to strengthen the linear relationship between: (1) the recorded and modeled joint moments, and (2) the muscle activations and the muscle forces. One male (age: 18 years; mass: 78 kg; height: 178 cm) participated in the study with the informed consent prior to commencing the experimental trials. Surface electrodes were attached to record EMG signals from elbow major muscles using an eight-channel surface EMG system (MyoSystem 1200, Noraxon Inc., USA). Dynamometer tasks were performed with Biodex System 3 Pro (Biodex Medical Systems, New York, USA) to measure elbow joint moments. The participant was asked to perform three maximum isometric contractions at 90° (flexed). The subject then generated an elbow flexion moment, rested, and generated an elbow extension moment. To evaluate the effects of NOF compared to EOF briefly, we focused on the changes in biceps brachii long head (BIClong) muscle force and compared the relative root-mean-square error.
Results and discussion
Even though NOF yielded relatively low performance in joint moment prediction, it estimated muscle forces better, providing more reasonable kinetic information about human movements such as walking and running.
This research was supported by the Human Resource Training Project for Regional Innovation through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013H1B8A2032194).
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