Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.

I. Introduction

Activity recognition of Activities of Daily Living (ADLs) has helped in the advancement of automated sensor systems for monitoring the well-being of the elderly population. In general, the detection of abnormal behaviour in ADLs can be an indicator of a progressive health problem (e.g. dementia, osteoporosis, arthritis) taking place or the occurrence of a hazardous incident (e.g. falls, burns, cuts, food/smoke intoxication). It is acknowledged that both ambient sensors (attached to objects in the environment with which users interact) and wearable sensors (worn by users in parts of their body or in their clothes) have advantages and disadvantages. However, it is also acknowledged that, with an adequate deployment and utilisation, both types of sensors deployed together can provide more insight that could allow a better activity recognition for monitoring ADLs than if they were utilised separately. This work continues the research presented in [5], [6] and [8], in which the data analysis did not consider the fusion of the data collected from the ambient and wearable sensors used.