Estimating Crew Alertness From Speech

2010 ◽  
Author(s):  
Parham Shahidi ◽  
Steve C. Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Speech Signal ◽  
High Speed ◽  
The United States ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Logic Algorithm ◽  
Novel Approach ◽  
The Government

With the latest initiative of the government to develop a high speed passenger rail system in the United States the first and most important strategic transportation goal is to “Ensure safe and efficient transportation choices. A key element of safe railroad operation is to address the issue of fatigue among railroad operating employees and how to fight it. In this paper, we are presenting a novel approach to estimating fatigue levels of train conductors by analyzing the speech signal in the communication between the conductor and dispatch. We extract vocal indicators of fatigue from the speech signal and use Fuzzy Logic to generate an estimate of the mental state of the train conductor. Previous research has shown that sleeping disorders, reduced hours of rest and disrupted circadian rhythms lead to significantly increased fatigue levels which manifest themselves in alterations of speech patterns as compared to alert states of mind. To make a decision about the level of fatigue, we are proposing a Fuzzy Logic algorithm which combines inputs such as word production rate and speech intensity to generate a Fatigue Quotient at any moment in time when speech is present. The computation of the Fatigue Quotient relies on a rule base which draws from existing knowledge about fatigue indicators and their relation to the level of fatigue of the subject. For this project, the rule base and the membership functions associated with it were derived from real time testing and the subsequent tuning of parameters to refine the detection of changes in patterns. It was successfully shown that Fuzzy Logic can be implemented to estimate alertness levels from speech metrics in real-time and that the membership functions for this purpose can be found empirically through iterative testing. Furthermore, this study has proven that the framework to run such an analysis continuously as a monitoring function in locomotive cabins is feasible and can be realized with relatively inexpensive hardware.

Estimating Changes in Speech Metrics Indicative of Fatigue Levels

2010 ◽  
Author(s):  
Parham Shahidi ◽  
Reza A. Soltan ◽  
Steve C. Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Probability Density ◽  
Speech Signal ◽  
Human Interaction ◽  
Risk And Uncertainty ◽  
Fuzzy Logic Algorithm ◽  
Novel Approach ◽  
Irreducible Uncertainty ◽  
Estimation System

In this paper, we are presenting a novel approach to estimate fatigue levels of train conductors, by analyzing the speech signal. An independent neural network joined with a Markov Model, will output the probability density, which illustrates the likelihood of the result of the first step to be accurate. Vigilance research has shown that, for most operators engaged in attention-intensive and monotonous tasks, retaining a constant level of alertness is almost impossible. Sleeping disorders, reduced hours of rest and disrupted circadian rhythms amplify this effect and lead to significantly increased fatigue levels. Increased fatigue levels manifest themselves in alterations of speech metrics, as compared to alert states of mind. To make a decision about the level of fatigue, we are proposing an alertness estimation system which uses speech metrics to generate a fatigue quotient indicative of the fatigue level. A speech pre-processor extracts metrics such as speech duration, word production rate and speech intensity from a continuous speech signal and uses a Fuzzy Logic algorithm to generate the fatigue quotient at any moment in time when speech is present. However, the nature of human interaction introduces levels of uncertainty, which make fatigue level recognition difficult. In other words, even with a perfectly trained neural network and Fuzzy Logic algorithm, we cannot make definite conclusions about the level of alertness. The reason being, that there is no guarantee that the estimated level of alertness is robust for a certain amount of time and didn’t come from drinking half a cup of coffee. Moreover, coming up with a perfect model of speech-fatigue (i.e. input-output) for humans, to train the Fuzzy algorithm is almost impossible. For this reason the study of “Risk and Uncertainty” is an integral part of this research. Motivated by the distinction between “risk” (randomness that can be fully captured by probability and statistics) and “uncertainty” (all other types of randomness), we propose a fine taxonomy: fully reducible, partially reducible, and irreducible uncertainty, that can explain some of the key differences between long term alertness and a short term change of state that makes the operator alert. An experimental study is conducted where a hyper articulated speech signal with three different levels of simulated fatigue is analyzed by the algorithm and a probability density function is assigned to the fatigue quotient to take the risk and uncertainty into account and make the overall result more reliable.

Big Data Deep Analytics for Geosocial Networks

2018 ◽  
pp. 120-140
Author(s):  
Muhammad Mazhar Ullah Rathore ◽  
Awais Ahmad ◽  
Anand Paul
Keyword(s):  
Big Data ◽  
Real Time ◽  
High Speed ◽  
Heterogeneous Data ◽  
Network Data ◽  
Social Network Data ◽  
Current Trends ◽  
The Government ◽  
Geosocial Networks

Geosocial network data provides the full information on current trends in human, their behaviors, their living style, the incidents and events, the disasters, current medical infection, and much more with respect to locations. Hence, the current geosocial media can work as a data asset for facilitating the national and the government itself by analyzing the geosocial data at real-time. However, there are millions of geosocial network users, who generates terabytes of heterogeneous data with a variety of information every day with high-speed, termed as Big Data. Analyzing such big amount of data and making real-time decisions is an inspiring task. Therefore, this book chapter discusses the exploration of geosocial networks. A system architecture is discussed and implemented in a real-time environment in order to process the abundant amount of various social network data to monitor the earth events, incidents, medical diseases, user trends and thoughts to make future real-time decisions as well as future planning.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

2013 ◽  
Vol 274 ◽  
pp. 345-349 ◽  
Author(s):  
Mei Lan Zhou ◽  
Deng Ke Lu ◽  
Wei Min Li ◽  
Hui Feng Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Membership Functions ◽  
Logic Control

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

scholarly journals A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations

2007 ◽  
Vol 24 (8) ◽  
pp. 1439-1451 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Pierre Tabary ◽  
Jacques Parent du Chatelet
Keyword(s):  
Fuzzy Logic ◽  
Case Studies ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Fuzzy Logic Algorithm

Abstract A fuzzy logic algorithm has been developed for the purpose of segregating precipitating from nonprecipitating echoes using polarimetric radar observations at C band. Adequate polarimetric descriptions for each type of scatterer are required for the algorithm to be effective. An observations-based approach is presented in this study to derive membership functions and objectively weight them so that they apply directly to conditions experienced at the radar site and to the radar wavelength. Three case studies are examined and show that the algorithm successfully removes nonprecipitating echoes from rainfall accumulation maps.

scholarly journals APPLICATION OF FUZZY LOGIC TOOLBOX FOR MODELLING FUZZY LOGIC CONTROLLERS

2017 ◽  
Vol 3 ◽  
pp. 539
Author(s):  
Krzysztof Olesiak
Keyword(s):  
Fuzzy Logic ◽  
Computer Technology ◽  
Control Surface ◽  
Rule Base ◽  
Membership Functions ◽  
Fuzzy Logic Controllers ◽  
Input And Output ◽  
Matlab Package ◽  
And Robotics ◽  
Logic Controllers

Computer technology, which has been developing very fast in the recent years, can be also fruitfully applied in teaching. For example, the software package Matlab is highly useful in teaching students at Bachelor Programs of Electrical Engineering and Automatics and Robotics. Fuzzy Logic Toolbox of the Matlab package can be used for designing and modelling controllers. Thanks to a large number of pre-defined elements available in the libraries, it is possible to create even highly complicated models of systems without much effort. Fuzzy Logic Toolbox is especially useful for exploring the basic rules of designing fuzzy logic controllers. The rules involve selecting input and output membership functions, determining their location with respect to one another and defining their ranges. When the membership functions are introduced, a rule base is defined and a defuzzification method is selected. For any defuzzification method, a control surface is obtained, which can be modified by changing the rule base and/or the input and output parameters of the membership function.

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

2021 ◽  
pp. 036119812110133
Author(s):  
Yuandong Liu ◽  
Zhihua Zhang ◽  
Lee D. Han ◽  
Candace Brakewood
Keyword(s):  
Real Time ◽  
High Speed ◽  
Recurrent Events ◽  
Clustering Algorithm ◽  
Selection Procedure ◽  
The United States ◽  
Streaming Data ◽  
Data Sources ◽  
Sensor Data ◽  
Temporal Extent

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

scholarly journals A Fuzzy Tuned and Second Estimator of the Optimal Quaternion Complementary Filter for Human Motion Measurement with Inertial and Magnetic Sensors

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3517 ◽  
Author(s):  
Xiaoyue Zhang ◽  
Wan Xiao
Keyword(s):  
Fuzzy Logic ◽  
High Speed ◽  
Algorithm Design ◽  
Maximum Error ◽  
Human Motion ◽  
Magnetic Sensors ◽  
Fuzzy Logic Algorithm ◽  
Complementary Filter ◽  
Optical Motion ◽  
Optical Motion Capture

To accurately measure human motion at high-speed, we proposed a simple structure complementary filter, named the Fuzzy Tuned and Second EStimator of the Optimal Quaternion Complementary Filter (FTECF). The FTECF is applicable to inertial and magnetic sensors, which include tri-axis gyroscopes, tri-axis accelerometers, and tri-axis magnetometers. More specifically, the proposed method incorporates three parts, the input quaternion, the reference quaternion, and the fuzzy logic algorithm. At first, the input quaternion was calculated with gyroscopes. Then, the reference quaternion was calculated by applying the Second EStimator of the Optimal Quaternion (ESOQ-2) algorithm on accelerometers and magnetometers. In addition, we added compensation for accelerometers in the ESOQ-2 algorithm so as to eliminate the effects of limb motion acceleration in high-speed human motion measurements. Finally, the fuzzy logic was utilized to calculate the fusion factor for a complementary filter, so as to adaptively fuse the input quaternion with the reference quaternion. Additionally, the overall algorithm design is more simplified than traditional methods. Confirmed by the experiments, using a commercial inertial and magnetic sensors unit and an optical motion capture system, the efficiency of the proposed method was more improved than two well-known methods. The root mean square error (RMSE) of the FTECF was less than 2.2° and the maximum error was less than 5.4°.

scholarly journals Double yolk eggs detection using fuzzy logic

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241888
Author(s):  
Thanasan Intarakumthornchai ◽  
Ramil Kesvarakul
Keyword(s):  
Fuzzy Logic ◽  
Low Cost ◽  
Total Error ◽  
Membership Functions ◽  
The Real ◽  
Fuzzy Logic Algorithm ◽  
Egg Products ◽  
Hard Clustering ◽  
The Cost ◽  
Chicken Eggs

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg’s size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg’s size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.

Sign in / Sign up

Export Citation Format

Share Document