An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer’s active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer’s leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient

Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%

Structurally Diverse Cytotoxic Dimeric Chalcones from <i>Oxytropis chiliophylla</i>

Ten isomeric cyclobutane- and cyclohexene-containing chalcone dimers, oxyfadichalcones A–G, were isolated from the aerial parts of <i>Oxytropis chiliophylla</i>. These included six new compounds and three pairs of enantiomers that are being reported from natural sources for the first time. The relative configurations were elucidated by spectroscopic data analysis, while the absolute configurations were determined by comparing the experimental and calculated electronic circular dichroism spectra. Quantitative LC-MS analysis of the main dimers from different parts of the plant revealed their characteristic accumulation in the viscous secretion and provided supporting evidence for the hypothesized photochemical biosynthesis. In addition, the cytotoxic activities of all isolates against the PC-3 human prostate cancer cell line are reported