2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDBIO-13.6
Paper Title CLASSIFICATION OF EXPERT-NOVICE LEVEL USING EYE TRACKING AND MOTION DATA VIA CONDITIONAL MULTIMODAL VARIATIONAL AUTOENCODER
Authors Yusuke Akamatsu, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionBIO-13: Deep Learning for Biomedical Applications
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing
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Virtual Presentation  Click here to watch in the Virtual Conference
Abstract 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.