Idiosyncratic repeatability of calibration errors during eye tracker calibration

Harężlak, Katarzyna; Kasprowski, Paweł; Stasch, Mateusz

doi:10.1109/hsi.2014.6860455

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…The HCI community has been harnessing artificial neural networks (ANNs), including recurrent and convolutional neural networks (RNNs and CNNs), for evaluating user behavior. Examples include discovering speech patterns [16], identifying gesture [39,50,60] and gaze [43,65], classifying user emotion and facial expression [25,41,58], and detecting characteristics of the user, such as gender [62], 4 by constructing and implementing neural network architecture. The visualization community has also made contributions to the toolkit of methods used in evaluating user video, logs, transcripts, and other qualitative data [12], as well as user gesture analysis [31].…”

Section: Characterizing Users With Machine Learningmentioning

confidence: 99%

uxSense: Supporting User Experience Analysis with Visualization and Computer Vision

Batch

Fan

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose UXSENSE, a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.

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