Intelligent data processing to support self-management and responsive care

This research is situated in the area of ambient intelligent systems for assisted living. The motivation for the research was to understand how ambient intelligent systems could be used to support people with learning disabilities in providing more personalised care, as well as function as an aid to support independent living. In the first phase of the research a series of interviews conducted with formal carers of people with learning disabilities highlighted kitchen activities as a potential area of support. This provided a focal point for the research whereby subsequent research involved the development of sensor data mining techniques and machine learning methods to recognise specific meal-preparation activities with a view to supporting task prompting. The goal of task prompting is to enable automated intervention for service users performing meal-preparation activities by tracking the activity in real-time by analysing ambient sensor data. In the second phase of the research a public smart-home dataset was used to develop a novel methodology which uses "temporal clusters" of sensor events as a pre-processing step for extracting features from the data and creating visualisations. In the third phase of the research, a data set comprising different meal-preparation activities undertaken by three participants in a shared kitchen was collected over a period of 8 weeks. This fully annotated dataset includes a combination of data from a range of ambient smart-home sensors and low-resolution thermal cameras. This dataset was used to experiment with knowledge-driven activity recognition techniques, which were used to develop a novel hybrid offline-online learning methodology for real-time activity recognition and prediction. This methodology is shown to overcome the shortcomings of existing supervised activity recognition methods, which require re-training with new data if the activity changes. The new methodology has been designed to enable learning from the user in order to track meal-preparation activities in real-time, detect deviations from the activity, and adapt to changes in the user's performance without requiring re-training. The research presented in this thesis, together with the meal-preparation dataset, are a crucial stepping-stone for the development of future technologies that offer the potential for real-time task prompting and thus could be useful in supporting people with learning disabilities in performing activities more independently. The approaches developed can also generate information that could help carers better understand how their service users are able to perform these activities and hence personalise and adapt the support they provide

Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods

© 2020, The Author(s). This paper investigates the utility of unsupervised machine learning and data visualisation for tracking changes in user activity over time. This is done through analysing unlabelled data generated from passive and ambient smart home sensors, such as motion sensors, which are considered less intrusive than video cameras or wearables. The challenge in using unlabelled passive and ambient sensors data for activity recognition is to find practical methods that can provide meaningful information to support timely interventions based on changing user needs, without the overhead of having to label the data over long periods of time. The paper addresses this challenge to discover patterns in unlabelled sensor data using kernel density estimation (KDE) for pre-processing the data, together with t-distributed stochastic neighbour embedding and uniform manifold approximation and projection for visualising changes. The methodology is developed and tested on the Aruba CASAS smart home dataset and focusses on discovering and tracking changes in kitchen-based activities. The traditional approach of using sliding windows to segment the data requires a priori knowledge of the temporal characteristics of activities being identified. In this paper, we show how an adaptive approach for segmentation, KDE, is a suitable alternative for identifying temporal clusters of sensor events from unlabelled data that can represent an activity. The ability to visualise different recurring patterns of activity and changes to these over time is illustrated by mapping the data for separate days of the week. The paper then demonstrates how this can be used to track patterns over longer time-frames which could be used tohelp highlight differences in the user’s day-to-day behaviour. By presenting the data in a format that can be visually reviewed for temporal changes in activity over varying periods of time from unlabelled sensor data, opens up the opportunity for carers to then initiate further enquiry if variations to previous patterns are noted. This is seen as an accessible first step to enable carers to initiate informed discussions with the service user to understand what may be causing these changes and suggest appropriate interventions if the change is found to be detrimental to their well-being