Extracting Health Insights from Smart Home Data: A Machine Learning Approach

Abstract

This research explores the potential of smart home sensor data to detect and monitor human activity patterns for healthcare applications. We utilize machine learning techniques to analyze large-scale datasets from various smart home devices (e.g., motion sensors, smart appliances) to identify patterns indicative of health conditions like falls, medication non-adherence, or changes in daily routines. The study demonstrates the feasibility of leveraging readily available smart home data for remote health monitoring and early intervention, showcasing improved accuracy compared to existing methods through the development of a novel algorithm. Future work will focus on expanding the range of detectable health conditions and improving the system's robustness.

Introduction

The aging global population and increasing demand for affordable healthcare necessitate innovative remote patient monitoring solutions. Smart homes, equipped with various sensors, offer a rich source of data reflecting daily living activities. Analyzing this "big data" can provide valuable insights into an individual's health status, enabling timely interventions. However, extracting meaningful patterns from this complex, high-dimensional data presents significant challenges, including noise reduction, feature selection, and the development of robust algorithms capable of handling individual variability. This research aims to address these challenges and develop a system for effectively monitoring health indicators through smart home data analysis.

Objectives

• Develop a robust algorithm for detecting health-relevant activity patterns from smart home data.
• Achieve high accuracy in predicting health conditions based on identified patterns.
• Design a user-friendly interface for visualizing health data and alerts.