Human Activity Recognition using Recurrent Neural Networks

TL;DR

This paper presents a deep learning approach using LSTM RNNs for human activity recognition in smart homes, demonstrating improved accuracy over existing methods on real datasets.

Contribution

The study introduces a novel application of LSTM RNNs for activity recognition without prior knowledge, outperforming previous models in accuracy and efficiency.

Findings

LSTM RNN achieved higher accuracy than traditional methods.

The approach effectively models temporal dependencies in sensor data.

Performance improvements were consistent across multiple datasets.

Abstract

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.