Date of Award

2017

Document Type

Thesis

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Science

First Supervisor

Dr Leisa Armstrong

Second Supervisor

Associate Professor Amiya Kumar Tripathy

Abstract

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored.

Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly.

To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel.

A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value.

Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns.

As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours.

The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs.

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