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

Caleb-Solly, Praminda; Gupta, Prankit; McClatchey, Richard

Abstract

© 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.

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2020
• Online Publication Date: Jan 25, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Jan 22, 2020
• Publicly Available Date: Jan 30, 2020
• Journal: Neural Computing and Applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 16
• Pages: 12351 - 12362
• DOI: https://doi.org/10.1007/s00521-020-04737-6
• Keywords: Human activity recognition, Unlabelled sensor data, Data visualisation, Unsupervised learning
• Public URL: https://uwe-repository.worktribe.com/output/5133600