Gesture Recognition Based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory Network for a Wearable Wrist Sensor with Multi-Walled Carbon Nanotube/Cotton Fabric Material.

Flexible pressure sensors play a crucial role in detecting human motion and facilitating human-computer interaction. In this paper, a type of flexible pressure sensor unit with high sensitivity (2.242 kPa-1), fast response time (80 ms), and remarkable stability (1000 cycles) is proposed and fabricated by the multi-walled carbon nanotube (MWCNT)/cotton fabric (CF) material based on a dip-coating method. Six flexible pressure sensor units are integrated into a flexible wristband and made into a wearable and portable wrist sensor with favorable stability. Then, seven wrist gestures (Gesture Group #1), five letter gestures (Gesture Group #2), and eight sign language gestures (Gesture Group #3) are performed by wearing the wrist sensor, and the corresponding time sequence signals of the three gesture groups (#1, #2, and #3) from the wrist sensor are collected, respectively. To efficiently recognize different gestures from the three groups detected by the wrist sensor, a fusion network model combined with a convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) neural network, named CNN-BiLSTM, which has strong robustness and generalization ability, is constructed. The three types of Gesture Groups were recognized based on the CNN-BiLSTM model with accuracies of 99.40%, 95.00%, and 98.44%. Twenty gestures (merged by Group #1, #2, and #3) were recognized with an accuracy of 96.88% to validate the applicability of the wrist sensor based on this model for gesture recognition. The experimental results denote that the CNN-BiLSTM model has very efficient performance in recognizing different gestures collected from the flexible wrist sensor.

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.

Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human-machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array.

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm.

Recognizing different contact patterns imposed on tactile sensors plays a very important role in human-machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) material. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN-LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor.

Decoupling Research of a Novel Three-Dimensional Force Flexible Tactile Sensor Based on an Improved BP Algorithm.

Decoupling research on flexible tactile sensors play a very important role in the intelligent robot skin and tactile-sensing fields. In this paper, an efficient machine learning method based on the improved back-propagation (BP) algorithm is proposed to decouple the mapping relationship between the resistances of force-sensitive conductive pillars and three-dimensional forces for the 6 × 6 novel flexible tactile sensor array. Tactile-sensing principles and numerical experiments are analyzed. The tactile sensor array model accomplishes the decomposition of the force components by its delicate structure, and avoids direct interference among the electrodes of the sensor array. The force components loaded on the tactile sensor are decoupled with a very high precision from the resistance signal by the improved BP algorithm. The decoupling results show that the k-cross validation (k-CV) algorithm is a highly effective method to improve the decoupling precision of force components for the novel tactile sensor. The large dataset with the k-CV method obtains a better decoupling accuracy of the force components than the small dataset. All of the decoupling results are fairly good, and they indicate that the improved BP model with a strong non-linear approaching ability has an efficient and valid performance in decoupling force components for the tactile sensor.

Formation and evolution of a pair of collisionless shocks in counter-streaming flows.

A pair of collisionless shocks that propagate in the opposite directions are firstly observed in the interactions of laser-produced counter-streaming flows. The flows are generated by irradiating a pair of opposing copper foils with eight laser beams at the Shenguang-II (SG-II) laser facility. The experimental results indicate that the excited shocks are collisionless and electrostatic, in good agreement with the theoretical model of electrostatic shock. The particle-in-cell (PIC) simulations verify that a strong electrostatic field growing from the interaction region contributes to the shocks formation. The evolution is driven by the thermal pressure gradient between the upstream and the downstream. Theoretical analysis indicates that the strength of the shocks is enhanced with the decreasing density ratio during both flows interpenetration. The positive feedback can offset the shock decay process. This is probable the main reason why the electrostatic shocks can keep stable for a longer time in our experiment.