Abstract：
:The standard camera- and force plate-based set-up for motion analysis suffers from the disadvantage of being limited to laboratory settings. Since adaptive algorithms are able to learn the connection between known inputs and outputs and generalise this knowledge to unknown data, these algorithms can be used to leverage motion analysis outside the laboratory. In most biomechanical applications, feedforward neural networks are used, although these networks can only work on time normalised data, while recurrent neural networks can be used for real time applications. Therefore, this study compares the performance of these two kinds of neural networks on the prediction of ground reaction force and joint moments of the lower limbs during gait based on joint angles determined by optical motion capture as input data. The accuracy of both networks when generalising to new data was assessed using the normalised root-mean-squared error, the root-mean-squared error and the correlation coefficient as evaluation metrics. Both neural networks demonstrated a high performance and good capabilities to generalise to new data. The mean prediction accuracy over all parameters applying a feedforward network was higher (r = 0.963) than using a recurrent long short-term memory network (r = 0.935).
journal_name
Med Eng Physjournal_title
Medical engineering & physicsauthors
Mundt M,Koeppe A,David S,Bamer F,Potthast W,Markert Bdoi
10.1016/j.medengphy.2020.10.001subject
Has Abstractpub_date
2020-12-01 00:00:00pages
29-34eissn
1350-4533issn
1873-4030pii
S1350-4533(20)30149-1journal_volume
86pub_type
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