Dynamics of Blink and Non-Blink Cyclicity for Affective Assessment: A Case Study for Stress Identification

Ren, Peng; Barreto, Armando; Ma, Xiaole; Liu, Shengnan; Zhang, Min; Wang, Ying; Dong, Yeyun; Yao, Dezhong

doi:10.1109/taffc.2019.2946829

Cited by 5 publications

(5 citation statements)

References 61 publications

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“…For instance, alpha waves disappear or gradually diminish during concentration lapses and drowsiness. On the other hand, if attention increases abnormally, the rate of alpha waves increases abnormally, too [75].…”

Section: ) Eeg Role In Stress Identificationmentioning

confidence: 99%

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Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

et al. 2021

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Heavy equipment operation is a responsible and difficult task causing mental workload on a human operator and exposing the operator to a range of harmful factors. Human factors and ergonomics in heavy equipment design have traditionally been focused on anthropometry and questionnaires. More advanced techniques involving biosignal measurements were not applied to heavy equipment, mainly due to the diversity of real working conditions that were hard to reproduce in a laboratory environment and that prevented ambulatory studies. Recent advances in wearable biosensors and real-time simulators produce the capability of using biosignals for improving the ergonomics of heavy equipment operation. The present paper reviews the use of biosignals in human factors and the ergonomics of heavy machines by focusing on stress detection for the last ten years. The aim of the paper is analyzing the previous implemented algorithms to find a set of biosignals and methods of stress identification that could be suitable for identifying stress in heavy equipment operators both in laboratory and ambulatory studies. The conclusion emphasizes successful stress identification methods and a combination of the algorithms from different studies that facilitate the use of heavy equipment operator's applications. Also, feasible methods and directions for future research are considered.

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Section: ) Eeg Role In Stress Identificationmentioning

confidence: 99%

“…Stimulators such as stress causes frequency changes in neural activity [75] and generates specific patterns in the EEG after onset of the stressor, namely event-related potentials (ERPs). For example, Brookhuis et al [76] designed a health monitoring system for driver workload using ECG and EEG signals.…”

Section: ) Eeg Role In Stress Identificationmentioning

confidence: 99%

Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

et al. 2021

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“…We're feeding it data and then training this to understand from it in order to make predictions. In the end, the algorithm produces whether any of the frames may be categorized as a feature or not by defining a minimal threshold [13][14][15][16].…”

Section: ) Training Classifiersmentioning

confidence: 99%

“…The goal function can be expressed formally as Eq. (16). A new SEEO model is used to fine-tune CNN's weight in order to achieve this objective.…”

Section: Calculate the New Shapementioning

confidence: 99%

“…The outcome from them is either blink detection or eye status reorganization (open/closed eye). Most of the eye-tracking and eye-blinking detection algorithms rely upon the video input [16]. However, the techniques developed for desktop environments are not suitable for mobile/smartphone environments, owing towards the major shortcoming in terms of computational resources.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

Gawande,

Badotra

2022

IJACSA

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In this research work, a novel eye-blink detection model is developed. The proposed eye blink detection model is modeled by following seven major phases: (a) video-to-frame conversion, (b) Pre-processing, (c) face detection, (d) eye region localization, (e) eye landmark detection and eye status detection, (f) eye blink detection and (f) Eye blink Classification. Initially, from the collected raw video sequence (input), each individual frames are extracted in the video -to-frame conversion phase. Then, each of the frames is subjected to pre-processing phase, where the quality of the image in the frames is improved using proposed Kernel median filtering (KMF) approach. In the face detection phase, the Viola-Jones Model has been utilized. Then, from the detected faces, the eye region is localization within the proposed eye region localization phase. The proposed Eye region localization phase encapsulates two major phases: Feature extraction and landmark detection. The features like improved active shape models (I-ASMs), Local Binary pattern are extracted from the detected facial images. Then, the eye region is localization by using a new optimized Convolution neural network framework. This optimized CNN framework is trained with the extracted features (I-ASM and LBP). Moreover, to enhance the classification accuracy of eye localization, the weight of CNN is fine-tuned using a new Seagull Optimization with Enhanced Exploration (SOEE), which is the improved version of standard Seagull Optimization Algorithm (SOA). The outcome from optimized CNN framework is providing the exact location of the eye region. Once the eye region is detected, it is essential to detect the status of the eye (whether open or close). The status of the eye is detected by computing the eye aspect ratio (EAR). Then, the identified eye blinks are classified based on the computed correlation coefficient as long and short blinks. Finally, a comparative evaluation has been accomplished to validate the projected model.

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Psychological stress recognition from heart rate variability parameters based on field programmable gate arrays

Wang

Luo

et al. 2022

Review of Scientific Instruments

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Psychological stress is a big threat to people’s health. Early detection of psychological stress is important. The design of a stress recognition device based on the ECG (electrocardiograph) signal is presented in this paper. The device features intelligence, precision, portability, fast response, and low power consumption. In the design, the ECG signals are acquired by the AD8232 ECG module and processed by a low power consumption FPGA (Field Programmable Gated Array) development board PYNQ-Z2. Meanwhile, a modified Deep Forest model named Aw-Deep Forest (Adaptive Weight Deep Forest) is proposed. The Aw-Deep Forest has better performance than the Deep Forest model because it improves the fitting quality of the forests. By implementing the Aw-Deep Forest model on the FPGA, the device can assess people’s state of psychological stress by analyzing the HRV (heart rate variability) parameters from ECG data. This paper mainly introduces the detailed process of ECG signal collecting, filtering, analog signal to digital signal conversion, HRV parameter analysis, and psychological stress recognition with Aw-Deep Forest. The final accuracy is 81.39%.

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Dynamics of Blink and Non-Blink Cyclicity for Affective Assessment: A Case Study for Stress Identification

Cited by 5 publications

References 61 publications

Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

Psychological stress recognition from heart rate variability parameters based on field programmable gate arrays

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