This is a repository copy of Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art.

Gait prediction is crucial in exoskeleton-assisted gait rehabilitation by recognizing the movement intention of patients, so as to realize the adaptive and transparent robotic assistance. Human locomotion has inherent synergies and coordination, and the dynamic mapping of the upper and lower limbs is beneficial to improve the prediction accuracy. Current prediction methods did not consider the correlation of gait data in time and space, resulting in a large amount of redundant data and low prediction accuracy. This paper proposes a gait trajectory prediction method based on convolutional neural network-long short-term memory (CNN-LSTM) model, which predicts the human knee/ankle joint trajectory based on upper and lower limb collaborative data. The attention mechanism is applied to determine which dimensions are essential in gait prediction, so the accuracy can be improved by adopting key elements. Results show that, within a predicted horizon of 50ms, the prediction RMSE is as low as 0.317 degrees.

The electrospray ionization tandem mass spectrometric (ESI-MS/MS) characteristics and fragmentation mechanisms of eight distamycin analogues containing N-methylpyrrole and N-methylimidazole were investigated. The members of two isomeric groups of distamycin analogues with the same elemental composition can be distinguished by MS/MS spectra of protonated molecules and of significant fragment ions.

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Zhang et alUse of CNN in esophageal protruded lesions