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• W4386159978 abstract "In this study, joint torques in the sagittal plane are estimated using joint angles and electromyography (EMG) signals during subjects' walk at 7 different speeds. First, a general inter-subject model is built by backpropagation neural network (BPNN) with data from 12 subjects. Then, to improve the estimation performance of the inter-subject for a new subject, sparse gaussian process (SGP) with residual estimation using input and output (RIO) kernel is used to compensate for the model as a transfer learning method. The obtained intra-subject model has superior performance with a relatively small amount of data in the training process. This article can be referenced when you have limited training data to estimate the torques on a new subject." @default.
• W4386159978 created "2023-08-26" @default.
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• W4386159978 date "2023-07-08" @default.
• W4386159978 modified "2023-09-24" @default.
• W4386159978 title "Lower Limb Joint Torque Estimation by Neural Network and Sparse Gaussian Process with RIO Kernel" @default.
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• W4386159978 doi "https://doi.org/10.1109/icarm58088.2023.10218774" @default.
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