# A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework

## TL;DR

A new system combines CNN-GRU and MRF to accurately recognize activities and indoor locations in smart homes, achieving high accuracy and robustness.

## Contribution

A novel hybrid CNN-GRU and MRF framework for joint indoor localization and activity recognition is introduced.

## Key findings

- The hybrid model achieved 95% accuracy for activity recognition and 93% for indoor localization.
- The system's combined activity-location classification accuracy was 81%.
- The framework is efficient and suitable for real-world healthcare and smart living applications.

## Abstract

This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencies, while the MRF models the mutual relations of activities and locations by estimating their joint probability distribution. The new system was tested on a public smart home dataset with four activities (sitting, lying, walking, and standing) and four indoor locations (kitchen, bedroom, living room, and stairs). The hybrid framework obtained an accuracy of 95% for activity recognition and 93% for indoor localization with a combined activity-location classification accuracy of 81%. Such results confirm the ability of the system to provide robust predictions in real-world smart environments, make it highly suitable for healthcare and intelligent living applications, and is efficient and deployable in real-world scenarios, addressing the critical challenges of noisy and dynamic indoor environments.