Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jun 29, 2020
Date Accepted: Dec 18, 2020
Deep Learning-Based Multi-Modal Data Fusion: A Case Study in Food Intake Episodes Detection Using Wearable Sensors
ABSTRACT
Multimodal wearable technologies have brought forward wide possibilities in human activity monitoring. As interpreting information coming from multiple sources requires data combining, current study presents a data fusion technique in a computationally efficient way to achieve comprehensive insight of human activity dynamics. In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep residual network model to learn specific patterns associated with specific activity. The method with final accuracy of 86.1% was evaluated using 2954 non-overlapping 500-sample sequences collected with Empatica E4 wristband.
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