HHAR-net: Hierarchical Human Activity Recognition using Neural Networks

TL;DR

This paper introduces HHAR-net, a hierarchical neural network model for human activity recognition that captures multiple activity layers, achieving high accuracy on real-world sensor data and outperforming baseline models.

Contribution

The paper proposes a novel hierarchical neural network approach for activity recognition, effectively modeling different activity levels and improving accuracy over existing methods.

Findings

Achieved 95.8% accuracy in recognizing stationary vs. non-stationary activities.

Attained 92.8% overall accuracy on six activity labels.

Model outperforms baseline by 3% in accuracy.

Abstract

Activity recognition using built-in sensors in smart and wearable devices provides great opportunities to understand and detect human behavior in the wild and gives a more holistic view of individuals' health and well being. Numerous computational methods have been applied to sensor streams to recognize different daily activities. However, most methods are unable to capture different layers of activities concealed in human behavior. Also, the performance of the models starts to decrease with increasing the number of activities. This research aims at building a hierarchical classification with Neural Networks to recognize human activities based on different levels of abstraction. We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches. We use a two-level hierarchy with a total of six mutually exclusive labels namely, "lying down", "sitting", "standing in place", "walking", "running", and "bicycling" divided into "stationary" and "non-stationary". The results show that our model can recognize low-level activities (stationary/non-stationary) with 95.8% accuracy and overall accuracy of 92.8% over six labels. This is 3% above our best performing baseline.