Latent: A Flexible Data Collection Tool to Research Human Behavior in the Context of Web Navigation

Cepeda, Cátia; Tonet, Ricardo; Osório, Daniel; Silva, Hugo Plácido da; Battegay, Edouard; Cheetham, Marcus; Gamboa, Hugo

doi:10.1109/access.2019.2916996

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Behavioural features, namely, Human–Computer Interaction (HCI) features were continuously extracted using Latent, a Chrome extension to track these features in the context of web-based navigation [ 31 ]. Mouse tracking, keyboard strokes, audio, snapshots, and screenshots were recorded and stored in a MongoDB for future analysis.…”

Section: Methodsmentioning

confidence: 99%

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Varandas

Lima

Badia

et al. 2022

Sensors

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Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91±13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.

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Section: Methodsmentioning

confidence: 99%

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Varandas

Lima

Badia

et al. 2022

Sensors

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“…Latent is a chrome extension used to track the HCI in contexts of web-based navigation [35]. This tool monitors and collects data of the mouse movement, keyboard presses, screenshots, audio, and snapshots using the computer's webcam during the Python tutorial, which involves extensive interaction with the computer.…”

Section: Data Acquisitionmentioning

confidence: 99%

Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

et al. 2022

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Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.

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“…A study by psychologist Mehrabian revealed that language only accounts for 7% of the information expressed in interpersonal interactions, whereas facial expressions express as much as 55%. This suggests that facial expressions play a significant role in human emotion judgment [1] and can be applied in a variety of scenarios, including medical treatment [2,3], fatigue driving detection [4][5][6][7], social robots [8][9][10], and other human-computer interaction systems [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Tang,

Zhang,

Tian

2023

Applied Sciences

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The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.

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Latent: A Flexible Data Collection Tool to Research Human Behavior in the Context of Web Navigation

Cited by 9 publications

References 19 publications

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

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