13350

Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors

Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang
Department of Electrical and Computer Engineering, Carnegie Mellon University, Moffett Field, CA, USA
Sixth International Conference on Mobile Computing, Applications and Services (MobiCASE 2014), 2014

@article{zeng2014convolutional,

   title={Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors},

   author={Zeng, Ming and Nguyen, Le T and Yu, Bo and Mengshoel, Ole J and Zhu, Jiang and Wu, Pang and Zhang, Joy},

   year={2014}

}

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A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.
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