# Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

arXiv: https://arxiv.org/abs/2106.15606 · HTML: https://mlsanity.com/paper/2106.15606/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 69 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [An iBeacon based Proximity and Indoor Localization System](https://mlsanity.com/paper/1703.07876/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept](https://mlsanity.com/paper/2207.11623/) (2022)
- [Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets](https://mlsanity.com/paper/2312.11895/) (2023)
- [Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments](https://mlsanity.com/paper/2207.09025/) (2022)

## Abstract (arXiv)

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user's indoor location in a specific activity-based zone during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the zone-based indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field, as per the associated root mean squared error - one of the performance evaluation metrics in ISO/IEC18305:2016- an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.