YAMAZOE HIROTAKE

This paper proposes a method of integrating known relations about the target task into a neural network as predefined computational modules. During the training process, the network is automatically induced to estimate the hidden parameters required by the embedded computational modules. In experiments, the proposed method was applied to 2D eyeball center position estimation. Experimental results confirmed that the accuracy of 2D eyeball center position estimation is improved and that the system acquires the ability to estimate 3D eye positions, which are hidden parameters.

Abstract Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.