What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural task

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Abstract
Deep learning models have shown remarkable performances in egocentric video-based action recognition (EAR), but rely heavily on a large quantity of training data. In specific applications with only limited data available, eye movement data may provide additional valuable sensory information to achieve accurate classification performances. However, little is known about the effectiveness of gaze data as a modality for egocentric action recognition. We, therefore, propose the new Peripheral Vision-Based HMM (PVHMM) classification framework, which utilizes context-rich and object-related gaze features for the detection of human action sequences. Gaze information is quantified using two features, the object-of-interest hit and the object–gaze distance, and human action recognition is achieved by employing a hidden Markov model. The classification performance of the framework is tested and validated on a safety-critical medical device handling task sequence involving seven distinct action classes, using 43 mobile eye tracking recordings. The robustness of the approach is evaluated using the addition of Gaussian noise. Finally, the results are then compared to the performance of a VGG-16 model. The gaze-enhanced PVHMM achieves high classification performances in the investigated medical procedure task, surpassing the purely image-based classification model. Consequently, this gaze-enhanced EAR approach shows the potential for the implementation in action sequence-dependent real-world applications, such as surgical training, performance assessment, or medical procedural tasks.
Funding Information
  • Innosuisse - Schweizerische Agentur für Innovationsförderung (43122.1)
  • Swiss Federal Institute of Technology Zurich

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