AAL Data Cluster Analysis. Theory and Implementation

Table of Contents

1. Introduction

1.1. Motivation

1.2. Outline

1.3. The Goal

1.4. Methodology

2. Theory Section

2.1. Gaussian Mixture Model

2.2. Dirichlet Process GMM

2.3. Hierarchical Clustering

3. Implementation

3.1. Software Specification

3.2. Data and Sensors

3.3. Data Visualisation and Inspection

3.4. Sensor values Discretisation and Extraction

3.5. Unsupervised Event Extraction

3.6. Data Structure for Event Analysis

3.7. Data Quality and Quantity

3.8. Predictive Analysis

3.8.1. Hourly Binning Analysis

3.8.2. Clustering Analysis

3.8.2.1. Hierarchical Clustering Analysis

3.8.2.2. Dirichlet Process Gaussian Mixture Model Clustering Analysis

4. Summary

4.1. Results

4.2. Discussion

Research Objectives and Themes

This thesis investigates unsupervised methods for predicting and clustering user behavior based on time-series data collected from infrared temperature sensors in an ambient assisted living (AAL) environment, with the aim of reliably identifying cooking activities.

• Adaptive intelligent sensor networks for elderly assistance.
• Unsupervised event detection from infrared signal data.
• Comparative analysis of prediction algorithms: Hourly Based Binning, Hierarchical Clustering, and Dirichlet Process GMM.
• Data preprocessing techniques including noise filtering, signal discretisation, and event sequencing.
• Evaluation of prediction performance based on data quality, quantity, and training set sizes.

Excerpt from the Book

Summary of Chapters

1. Introduction: Presents the motivation for AAL systems and defines the goal of detecting elderly cooking patterns without supervised intervention.

2. Theory Section: Discusses the theoretical foundations of Gaussian Mixture Models, Dirichlet Process GMM, and Hierarchical Clustering.

3. Implementation: Describes the practical setup, sensor data formats, signal discretization, and the execution of predictive analysis using different clustering and binning approaches.

4. Summary: Evaluates the performance of the implemented methods and discusses findings regarding data quality and future improvements for adaptive systems.

Keywords

AAL, Data Cluster Analysis, Predictive Analysis, Gaussian Mixture Model, DPGMM, Hierarchical Clustering, Hourly Binning, Time-Series Data, Infrared Sensors, User Behaviour, Unsupervised Learning, Activity Monitoring, Sensor Data Discretisation, Event Extraction, Smart Homes

Frequently Asked Questions

What is the primary focus of this research?

The research focuses on analyzing time-series sensor data from private households to identify and predict cooking activities of older people to support independent living.

What are the main thematic fields addressed?

The work combines ambient assisted living (AAL) technologies, machine learning for clustering, and data preprocessing for predictive modeling.

What is the central research question?

The thesis aims to determine how reliable and unsupervised prediction of daily user behavior can be achieved using various clustering and binning algorithms on raw infrared sensor signals.

Which scientific methods are employed?

The author evaluates Hourly Based Binning against more complex unsupervised clustering techniques, specifically Hierarchical Clustering and Dirichlet Process Gaussian Mixture Models (DPGMM).

What does the implementation part cover?

It details the software environment (Python/IPython), data cleansing of noisy CSV inputs, event isolation, sequence naming, and the comparison of different algorithms against training and testing datasets.

Which criteria are used to evaluate the methods?

The methods are evaluated based on prediction hit rates, computational efficiency, robustness regarding sparse/noisy data, and the ability to find a realistic number of clusters.

How does the DPGMM approach compare to the other methods?

DPGMM shows robustness with smaller, sparse datasets, though it often requires parameter tuning to outperform the simpler Hourly Binning approach.

Why did the author use Hierarchical Clustering?

It was chosen as an unsupervised grouping method that requires fewer parameters than other models and serves as a baseline to pre-estimate cluster counts for more complex models like DPGMM.

What impact does data quality have on the results?

The study concludes that prediction performance is directly correlated with data density and recording duration, with smaller or noisier datasets leading to lower accuracy and higher miss rates.