Human activity recognition for static and dynamic activity using convolutional neural network

Abstract

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97%. The result indicated the ability of the model for human activity recognition purposes.

New blender-based augmentation method with quantitative evaluation of CNNs for hand gesture recognition

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End-to-end multiple modals deep learning system for hand posture recognition

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