  Traditional human activity recognition (HAR) based on time series adopts
sliding window analysis method. This method faces the multi-class window
problem which mistakenly labels different classes of sampling points within a
window as a class. In this paper, a HAR algorithm based on U-Net is proposed to
perform activity labeling and prediction at each sampling point. The activity
data of the triaxial accelerometer is mapped into an image with the single
pixel column and multi-channel which is input into the U-Net network for
training and recognition. Our proposal can complete the pixel-level gesture
recognition function. The method does not need manual feature extraction and
can effectively identify short-term behaviors in long-term activity sequences.
We collected the Sanitation dataset and tested the proposed scheme with four
open data sets. The experimental results show that compared with Support Vector
Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic
Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully
Convolutional Networks (FCN) methods, our proposal has the highest accuracy and
F1-socre in each dataset, and has stable performance and high robustness. At
the same time, after the U-Net has finished training, our proposal can achieve
fast enough recognition speed.
