Human activity recognition based on time series analysis using U-Net

TL;DR

This paper introduces a U-Net based method for human activity recognition from time series data, enabling pixel-level activity labeling without manual feature extraction, and demonstrating superior accuracy and robustness across multiple datasets.

Contribution

The paper proposes a novel U-Net based approach for HAR that performs activity labeling at each sampling point, overcoming the multi-class window problem of traditional methods.

Findings

Achieves higher accuracy and F1-score than traditional classifiers and CNNs.

Provides stable and robust performance across four datasets.

Enables fast recognition after training.

Abstract

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.