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This paper describes a deep learning approach to classify physically fatigued and nonfatigued gait cycles via a recurrent neural network (RNN), where each gait cycle is represented as a time series of three-dimensional coordinates of body joints. Gait cycles inherently have large intra-class variations caused by gait stance differences (e.g., which foot is supporting/swinging) at the beginning of each gait cycle, which makes it difficult to identify subtle differences induced by fatigue. To overcome these difficulties, we introduce a supporting foot-aware RNN model in a multi-task learning framework for better fatigue detection. More specifically, the RNN model has two branches of layers: one is assigned to the main task of fatigue classification and the other is assigned to the auxiliary task of estimating the first supporting foot in the gait cycles. We collected physically fatigued and non-fatigued gait cycles from eight subjects and conducted experiments to evaluate the accuracies of the proposed multi-task model in comparison to a single-task model. As a result, the proposed method achieved an overall area under curve (AUC) of 0.860 for fatigue classification in a leave-one-subject-out cross-validation, and an AUC of 0.915 in a leave-oneday-out evaluation. It can be concluded from the experimental results that a fatigue detection system for daily use, especially for screening purposes, is very feasible on the basis of the proposed approach.INDEX TERMS Fatigue detection, gait behavior, multi-task learning, recurrent neural network, skeletal joint coordinates

We describe a MPEG-7 Meta-Data enhanced Audio-Visual Encoder system that targets DVD recorders. We extract features in the compressed domain with both video and audio, which allows us to add the meta-data extraction without altering the hardware architecture of the encoder core. Our feature extraction algorithms are simple, and thus implementable through a simple combination of software and hardware on the integrated DVD chip. The primary application of the meta-data is video summarization, which enables rapid browsing of stored video by the end user. The simplicity of our summarization and feature extraction algorithms enables incorporation of the powerful functionality of smart content navigation through content summarization, into the DVD recorder at a low cost.This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTWe describe a MPEG-7 Meta-Data enhanced Audio-Visual Encoder system that targets DVD recorders. We extract features in the compressed domain with both video and audio, which allows us to add the meta-data extraction without altering the hardware architecture of the encoder core. Our feature extraction algorithms are simple, and thus implementable through a simple combination of software and hardware on the integrated DVD chip. The primary application of the meta-data is video summarization, which enables rapid browsing of stored video by the end user. The simplicity of our summarization and feature extraction algorithms enables incorporation of the powerful functionality of smart content navigation through content summarization, into the DVD recorder at a low cost.

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