Fatigue Detection Using Artificial Intelligence Framework

Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

The prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains.

The value of a spaceflight clinical decision support system for earth-independent medical operations

As NASA prepares for crewed lunar missions over the next several years, plans are also underway to journey farther into deep space. Deep space exploration will require a paradigm shift in astronaut medical support toward progressively earth-independent medical operations (EIMO). The Exploration Medical Capability (ExMC) element of NASA's Human Research Program (HRP) is investigating the feasibility and value of advanced capabilities to promote and enhance EIMO. Currently, astronauts rely on real-time communication with ground-based medical providers. However, as the distance from Earth increases, so do communication delays and disruptions. Moreover, resupply and evacuation will become increasingly complex, if not impossible, on deep space missions. In contrast to today's missions in low earth orbit (LEO), where most medical expertise and decision-making are ground-based, an exploration crew will need to autonomously detect, diagnose, treat, and prevent medical events. Due to the sheer amount of pre-mission training required to execute a human spaceflight mission, there is often little time to devote exclusively to medical training. One potential solution is to augment the long duration exploration crew's knowledge, skills, and abilities with a clinical decision support system (CDSS). An analysis of preliminary data indicates the potential benefits of a CDSS to mission outcomes when augmenting cognitive and procedural performance of an autonomous crew performing medical operations, and we provide an illustrative scenario of how such a CDSS might function.

Developing AI enabled sensors and decision support for military operators in the field

Wearable sensors enable down range data collection of physiological and cognitive performance of the warfighter. However, autonomous teams may find the sensor data impractical to interpret and hence influence real-time decisions without the support of subject matter experts. Decision support tools can reduce the burden of interpreting physiological data in the field and incorporate a systems perspective where noisy field data can contain useful additional signals. We present a methodology of how artificial intelligence can be used for modeling human performance with decision-making to achieve actionable decision support. We provide a framework for systems design and advancing from the laboratory to real world environments. The result is a validated measure of down-range human performance with a low burden of operation.

DCCL: Dual-channel hybrid neural network combined with self-attention for text classification

Text classification is a fundamental task in natural language processing. The Chinese text classification task suffers from sparse text features, ambiguity in word segmentation, and poor performance of classification models. A text classification model is proposed based on the self-attention mechanism combined with CNN and LSTM. The proposed model uses word vectors as input to a dual-channel neural network structure, using multiple CNNs to extract the N-Gram information of different word windows and enrich the local feature representation through the concatenation operation, the BiLSTM is used to extract the semantic association information of the context to obtain the high-level feature representation at the sentence level. The output of BiLSTM is feature weighted with self-attention to reduce the influence of noisy features. The outputs of the dual channels are concatenated and fed into the softmax layer for classification. The results of the multiple comparison experiments showed that the DCCL model obtained 90.07% and 96.26% F1-score on the Sougou and THUNews datasets, respectively. Compared to the baseline model, the improvement was 3.24% and 2.19%, respectively. The proposed DCCL model can alleviate the problem of CNN losing word order information and the gradient of BiLSTM when processing text sequences, effectively integrate local and global text features, and highlight key information. The classification performance of the DCCL model is excellent and suitable for text classification tasks.

Non-Contact Negative Mood State Detection Using Reliability-Focused Multi-Modal Fusion Model

Negative mood states include tension, depression, anger, fatigue, and confusion, which represent the weak internal emotions of a human. Negative mood states exert adverse impact on individuals' ability to make rational decisions, which entails the practicable method of negative mood state detection. The most commonly used negative mood state detection methods are based on the psychological scale, which requires additional work and brings inconvenience to the subject in the application scenarios. To overcome this challenge, this paper proposes a novel non-contact negative mood state detection method according to the knowledge of affective computing. The POMS-net model is used to extract temporal-spatial features from visible and infrared thermal videos, and the negative mood state detection is realized using data reliability-focused multi-modal fusion. The proposed method is verified using the HDT-BR dataset collected in the aerospace medicine experiment "Earth-Star II" and the VIRI public dataset. The experimental results on the datasets verify that our method outperforms the comparison methods.

Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.

Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm's research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a "machine" that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal

BACKGROUND

Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features.

NEW-METHOD

Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method.

RESULTS

The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively.

COMPARISON-WITH-EXISTING-METHOD

The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features.

CONCLUSIONS

Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.