Файл:Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks ACMMM2015 ActivityRecognition.pdf

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Human_Activity_Recognition_using_Wearable_Sensors_by_Deep_Convolutional_Neural_Networks_ACMMM2015_ActivityRecognition.pdf(0 × 0 пикселей, размер файла: 2,81 МБ, MIME-тип: application/pdf)
  • Wenchao Jiang Department of Computer Science Missouri University of Science and Technology wjm84@mst.edu
  • Zhaozheng Yin Department of Computer Science Missouri University of Science and Technology yinz@mst.edu


ABSTRACT

Human physical activity recognition based on wearable sensors has applications relevant to our daily life such as healthcare. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it outperforms state-of-the-arts in terms of recognition accuracy and computational cost.

Categories and Subject Descriptors

  • I.2 [Computing Methodologies ]: Artificial Intelligence;
  • F.1.1 [Theory of Computation]: Models of Computation—Self-modifying machines

Keywords Wearable Computing, Activity Recognition, Deep Convolutional Neural Networks, Activity Image.

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