Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

Idongesit Ekerete,Christopher Nugent,Mustafa A Mustafa,James Mclaughlin,Yohanca Diaz-Skeete,Matias Garcia-Constantino,Alexandros Konios

doi:10.3390/app11199096

Abstract

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.

Highlights

Recognising individual activities of people susceptible to hazardous behaviours such as falls, wandering, and agitation has been an active research topic, which has witnessed the use of pervasive and non-pervasive Sensing Solutions (SSs) [1]
This may be achieved by using Data Mining (DM) and Machine Learning (ML) models, which can help discover patterns and potential deviations from established patterns in the data gleaned from a sensorised environment
Whilst the hot kettle was represented as a large blob adjacent to the participant, the tea/coffee cup was viewed as a small bright spot in what could be viewed as the hand of the user (Figure 5)

Summary

Introduction

Recognising individual activities of people susceptible to hazardous behaviours such as falls, wandering, and agitation has been an active research topic, which has witnessed the use of pervasive and non-pervasive Sensing Solutions (SSs) [1]. While there are several SSs that can detect these behaviours when they occur, it would be of great benefit if they can be predicted prior to their occurrence This may be achieved by using Data Mining (DM) and Machine Learning (ML) models, which can help discover patterns and potential deviations from established patterns in the data gleaned from a sensorised environment. Its outcome helps to determine if an ageing individual can be considered to be independent or not whilst performing certain activities [4]. This is an important part of the home-based assessment process to gauge if a person can remain living in their own home. The present work benefits from cluster-based analysis of patterns discovered from features extracted from thermal images using DM models and methods

Methods

Results

Conclusion