scholarly journals Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4054 ◽  
Author(s):  
Fernandez-Lopez ◽  
Liu-Jimenez ◽  
Kiyokawa ◽  
Wu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Sensors ◽  
Selection Process ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Tuning Method ◽  
Random Grid ◽  
Hyperparameter Selection ◽  
User Recognition

In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) for hyperspectral image based face recognition in real time door locking applications

2021 ◽  
pp. 1-11
Author(s):  
Ashok Kumar Rai ◽  
Radha Senthilkumar ◽  
A. Kanan
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recurrent Neural Network ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Facial Feature ◽  
Recognition System ◽  
Training Data ◽  
Search Optimization ◽  
Feature Extraction And Selection

Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods.

scholarly journals Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6900
Author(s):  
Angel Peinado-Contreras ◽  
Mario Munoz-Organero
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Deep Neural Network ◽  
Biometric Identification ◽  
Vertical Acceleration ◽  
Acceleration Data ◽  
Deep Recurrent Neural Network ◽  
Dense Output ◽  
User Recognition

This manuscript presents an approach to the challenge of biometric identification based on the acceleration patterns generated by a user while walking. The proposed approach uses the data captured by a smartphone’s accelerometer and gyroscope sensors while the users perform the gait activity and optimizes the design of a recurrent neural network (RNN) to optimally learn the features that better characterize each individual. The database is composed of 15 users, and the acceleration data provided has a tri-axial format in the X-Y-Z axes. Data are pre-processed to estimate the vertical acceleration (in the direction of the gravity force). A deep recurrent neural network model consisting of LSTM cells divided into several layers and dense output layers is used for user recognition. The precision results obtained by the final architecture are above 97% in most executions. The proposed deep neural network-based architecture is tested in different scenarios to check its efficiency and robustness.

Gait Recognition Using Deep Learning

2020 ◽  
pp. 214-226 ◽  
Author(s):  
Chaoran Liu ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Gait Recognition ◽  
Feature Learning ◽  
Recognition Algorithm ◽  
Boltzmann Machine ◽  
Histogram Of Oriented Gradient ◽  
Algorithm Framework ◽  
Efficient Gait

Gait recognition mainly uses different postures of each individual to perform identity authentication. In the existing methods, the full-cycle gait images are used for feature extraction, but there are problems such as occlusion and frame loss in the actual scene. It is not easy to obtain a full-cycle gait image. Therefore, how to construct a highly efficient gait recognition algorithm framework based on a small number of gait images to improve the efficiency and accuracy of recognition has become the focus of gait recognition research. In this chapter, deep neural network CRBM+FC is created. Based on the characteristics of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) fusion, a method of learning gait recognition from GEI to output is proposed. A brand-new gait recognition algorithm based on layered fu-sion of LBP and HOG is proposed. This chapter also proposes a feature learning network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).

scholarly journals A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 100954-100963
Author(s):  
Xiaofang Pan ◽  
Haien Zhang ◽  
Wenbin Ye ◽  
Amine Bermak ◽  
Xiaojin Zhao
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recognition Algorithm

scholarly journals An Automatic Assessment System for Alzheimer’s Disease Based on Speech Using Feature Sequence Generator and Recurrent Neural Network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yi-Wei Chien ◽  
Sheng-Yi Hong ◽  
Wen-Ting Cheah ◽  
Li-Hung Yao ◽  
Yu-Ling Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Cognitive Skills ◽  
Selection Process ◽  
Characteristic Curve ◽  
Optimal Solution ◽  
Assessment System ◽  
Automatic Assessment ◽  
Data Driven Approach ◽  
Feature Sequence

AbstractAlzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker’s cognitive skills abundantly and data collection is relatively inexpensive compared with brain imaging, blood testing, etc. While most of the existing literature extracted statistics-based features and relied on a feature selection process, we have proposed a novel Feature Sequence representation and utilized a data-driven approach, namely, the recurrent neural network to perform classification in this study. The system is also shown to be fully-automated, which implies the system can be deployed widely to all places easily. To validate our study, a series of experiments have been conducted with 120 speech samples, and the score in terms of the area under the receiver operating characteristic curve is as high as 0.838.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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