The Influence of Experience on Gazing Patterns during Endovascular Treatment: Eye-Tracking Study.

Objective: In various fields, differences in eye-gazing patterns during tasks between experts and novices have been evaluated. The aim of this study was to investigate gazing patterns during neuro-endovascular treatment using an eye-tracking device and assess whether gazing patterns depend on the physician's experience or skill. Methods: Seven physicians performed coil embolization for a cerebral aneurysm in a silicone vessel model under biplane X-ray fluoroscopy, and their gazing patterns were recorded using an eye-tracking device. The subjects were divided into three groups according to experience, highly experienced (Expert) group, intermediately experienced (Trainee) group, and less experienced (Novice) group. The duration of fixation on the anterior-posterior (AP) view screen, lateral (LR) view, and out-of-screen were compared between each group. Results: During microcatheter navigation, the Expert and Trainee groups spent a long time on fixation to AP, while the Novice group split their attention between each location. In coil insertion, the Expert group gazed at both the AP and the LR views with more saccades between screens. In contrast, the Trainee group spent most of their time only on the AP view screen and the Novice group spent longer out-of-screen. Conclusion: An eye-tracking device can detect different gazing patterns among physicians with several experiences and skill levels of neuroendovascular treatment. Learning the gazing patterns of experts using eye tracking may be a good educational tool for novices and trainees.

DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor.

The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically: 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.

WaistonBelt X: A Belt-Type Wearable Device with Sensing and Intervention Toward Health Behavior Change.

Changing behavior related to improper lifestyle habits has attracted attention as a solution to prevent lifestyle diseases, such as diabetes, heart disease, arteriosclerosis, and stroke. To drive health behavior changes, wearable devices are needed, and they must not only provide accurate sensing and visualization functions but also effective intervention functions. In this paper, we propose a health support system, WaistonBelt X, that consists of a belt-type wearable device with sensing and intervention functions and a smartphone application. WaistonBelt X can automatically measure a waistline with a magnetometer that detects the movements of a blade installed in the buckle, and monitor the basic activities of daily living with inertial sensors. Furthermore, WaistonBelt X intervenes with the user to correct lifestyle habits by using a built-in vibrator. Through evaluation experiments, we confirmed that our proposed device achieves measurement of the circumference on the belt position (mean absolute error of 0.93 cm) and basic activity recognition (F1 score of 0.95) with high accuracy. In addition, we confirmed that the intervention via belt vibration effectively improves the sitting posture of the user.

EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data.

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds.