Classification of Expert-Novice Level Using Eye Tracking And Motion Data via Conditional Multimodal Variational Autoencoder

Sensor data from wearable devices have been utilized to analyze differences between experts and novices. Previous studies attempted to classify the expert-novice level from sensor data based on supervised learning methods. However, these approaches need to collect enough training data covering vario...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1360 - 1364
Main Authors: Akamatsu, Yusuke, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 06.06.2021
Subjects:
anomaly detection
expert-novice level
Gaze tracking
Multimodal sensors
sensor data
Signal processing
Supervised learning
Training
Training data
variational autoencoder
Wearable computers
Wearable devices
ISSN:2379-190X
Online Access:Get full text
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Summary:Sensor data from wearable devices have been utilized to analyze differences between experts and novices. Previous studies attempted to classify the expert-novice level from sensor data based on supervised learning methods. However, these approaches need to collect enough training data covering various novices' sensor patterns. In this paper, we propose a semi-supervised anomaly detection approach that requires only sensor data of experts for training and identifies those of novices as anomalies. Our proposed anomaly detection model named conditional multimodal variational autoencoder (CMVAE) has the following two technical contributions: (i) considering action information of persons and (ii) utilizing multimodal sensor data, i.e., eye tracking data and motion data in this case. The proposed method is evaluated on sensor data measured when expert and novice soccer players were shooting, dribbling, and doing soccer ball juggling. Experimental results show that CMVAE can more accurately classify the expert-novice level than previous supervised learning methods and anomaly detection methods using other VAEs.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414361