Indoor Location Data for Tracking Human Behaviours: A Scoping Review

Abstract

Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.

Keywords: computational intelligence; data analytics; digital phenotyping; health monitoring technologies; human behaviour; real-time location systems; sensor-based assessments.

Figure 1

Flow of sources of literature through the paper screening process.

Figure 2

Behavioural outcomes in studies using RTLS for health status monitoring. The percent (number) of studies in each outcome category are provided, and the number of overlapping studies are provided in brackets in the areas of overlap.

Figure 3

Breakdown of the feature categories and a list of the features observed within each category. The symbols described in the legend represent a binary indicator of whether one or more papers from the application sector denoted used the listed feature. Detailed results for each feature category with study references can be found in supplementary files.