scholarly journals Requirements for the Procedure for Assessing the Operator’s Person When Processing Accelerometer Data

2021 ◽  
Vol 2096 (1) ◽  
pp. 012118
Author(s):  
A V Grecheneva ◽  
N V Dorofeev ◽  
M S Goryachev
Keyword(s):  
Neural Network ◽  
Portable Device ◽  
Accelerometer Data ◽  
Biometric Authentication ◽  
Intelligent Processing ◽  
Informative Parameters ◽  
Gait Parameters ◽  
Gait Biometrics ◽  
Neural Network Algorithm ◽  
Single Sensor

Abstract The article considers the possibility of biometric authentication based on gait parameters, which are obtained after intelligent processing of the accelerometer data of a wearable device. The article discusses the main trends and trends in the field of biometric authentication, as well as authentication by gait parameters. The developed neural network algorithm and informative parameters are described in the authentication procedure based on the data of a single sensor of a portable device. The practical verification of the proposed approach is carried out on 32 subjects of different physiology. The results of the study show the possibility of distinguishing their own movements in 100% of cases, and the distinction of the subjects is more than 90%. Also, the final part of the article provides the requirements for the authentication procedure when processing accelerometric data of gait biometrics, the level of trust of the developed algorithm is determined.

scholarly journals Classification of movements based on wearable device data in biometric authentication

2021 ◽  
Vol 2094 (3) ◽  
pp. 032017
Author(s):  
A V Grecheneva ◽  
N V Dorofeev
Keyword(s):  
Neural Network ◽  
Mobile Device ◽  
Single Step ◽  
Mobile Phone Data ◽  
Accelerometer Data ◽  
Biometric Authentication ◽  
Intelligent Algorithms ◽  
Human Movements ◽  
Neural Network Algorithm

Abstract The paper proposes a neural network algorithm for classifying human movements according to the accelerometer data, which is located in a mobile device. Intelligent algorithms for classifying movement types (single step, walking, walking on stairs, running) are considered on 9 types of different movements that a person performs in everyday life. The developed algorithm is proposed to be used in biometric authentication systems based on mobile phone data.

scholarly journals Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3906
Author(s):  
Seunghyun Oh ◽  
Chanhee Bae ◽  
Jaechan Cho ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Continuous Wave ◽  
Low Complexity ◽  
Cluttered Environments ◽  
Radar Sensors ◽  
Wave Radar ◽  
Single Voice ◽  
Neural Network Algorithm ◽  
Single Sensor

Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system.

scholarly journals An Artificial Neural Network Algorithm for the Evaluation of Postoperative Rehabilitation of Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Kunhao Tang ◽  
Ruogu Luo ◽  
Sanhua Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Postoperative Rehabilitation ◽  
Intelligence Algorithm ◽  
Gait Parameters ◽  
Gait Characteristic ◽  
Neural Network Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Artificial Neural

In order to explore the application of artificial neural network in rehabilitation evaluation, a kind of ANN stable and reliable artificial intelligence algorithm is proposed. By learning the existing clinical gait data, this method extracted the gait characteristic parameters of patients with different ages, disease types and course of disease, and repeated data iteration and finally simulated the corresponding gait parameters of patients. Experiments showed that the trained ANN had the same score as the human for most of the data (82.2%, Cohen’s kappa = 0.743). There was a strong correlation between ANN and improved Ashworth scores as assessed by human raters (r = 0.825, P < 0.01 ). As a stable and reliable artificial intelligence algorithm, ANN can provide new ideas and methods for clinical rehabilitation evaluation.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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