Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network

Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.

Section: Preprocessingmentioning

confidence: 99%

Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor

Xia

Wang

et al. 2020

“…LSTM is an advanced recurrent neural network (RNN) structure that can learn and predict time series data [ 20 , 21 ]. For an ordinary RNN network, the output at the time t is as follows:

where

is the input at the current moment;

is the state of the network at time t , which is derived from the output of the network at the previous moment (i.e.,

);

is the weight matrix of the input;

is the weight matrix for the states;

is the bias; and

is the activation function of the network.…”

Section: Usv Attitude Predictionmentioning

confidence: 99%

An Attitude Prediction Method for Autonomous Recovery Operation of Unmanned Surface Vehicle

Yang

Ping

Jiang

et al. 2020

The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by “Jinghai VII” USV developed by Shanghai University, China, under levels 1–4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments.

“…A two-stage training strategy is used to first train the CNN and then fine tune the whole CNN + LSTM network. Ma et al [33] focused on addressing noisy skeleton sequences and proposed a LSTM network together with a nested interval unscented Kalman filter (UKF) to improve performance for noisy datasets.…”

Section: Related Workmentioning

confidence: 99%

“…The temporal information is not fully exploited. Another family of solutions utilizes recurrent neural networks (RNN) to process the input hand skeleton sequences and predict the gesture class [32,33]. However, these methods only treat the raw skeleton sequences as input and do not fully leverage the properties of dynamic hand gestures, whose most important clues are articulated movements of fingers and the global movements of the hand.…”

Section: Introductionmentioning

confidence: 99%

MFA-Net: Motion Feature Augmented Network for Dynamic Hand Gesture Recognition from Skeletal Data

Chen

Wang

Guo³

et al. 2019

Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction. In this paper, we propose a novel motion feature augmented network (MFA-Net) for dynamic hand gesture recognition from skeletal data. MFA-Net exploits motion features of finger and global movements to augment features of deep network for gesture recognition. To describe finger articulated movements, finger motion features are extracted from the hand skeleton sequence via a variational autoencoder. Global motion features are utilized to represent the global movements of hand skeleton. These motion features along with the skeleton sequence are then fed into three branches of a recurrent neural network (RNN), which augment the motion features for RNN and improve the classification performance. The proposed MFA-Net is evaluated on two challenging skeleton-based dynamic hand gesture datasets, including DHG-14/28 dataset and SHREC’17 dataset. Experimental results demonstrate that our proposed method achieves comparable performance on DHG-14/28 dataset and better performance on SHREC’17 dataset when compared with start-of-the-art methods.