• Histograms
  • Stick Man Diagram of beacons and smart devices

BESiDE Method for Sensor-Based Analysis of Building Use in Care Homes


The BESiDE team have developed a system for capturing quantitative data about how older adults utilise the indoor spaces in residential care homes. The system uses sensors and smart devices to collect data about residents’ movement and activities, and was successfully used in multiple studies in a number of homes.

The instrumentation was successfully installed in multiple care homes and data was successfully collected from a number of residents over several days.

What can this sort of data tell us?

The automated data collection system was intended to allow us to answer the following questions about the participating care home residents

•       Where do the residents spend most of their time?

•       Where are the residents most physically active?

•       Where do most social interactions between residents/staff/visitors take place?

•       Do factors such as temperature, ambient noise have any effect on resident behaviour?

The Data Collection System

The system developed by the BESiDE Team uses a number of low-energy Bluetooth transmitters (‘beacons’), which are placed throughout the public areas of the care home. Once the sensors are in place and calibration data has been collected, residents are given the smart devices and an application on the device tracks the data from various sources automatically over the course of the study. At the end of the study, the devices are retrieved and the data is uploaded to the server for processing. 

Participant Data

The following data is collected about each participant in the study:

Location Data

Each beacon transmits a Bluetooth signal. The smart device records the signal strength from all of the nearby beacons and this data can be used to estimate the position of the user within one of the instrumented rooms within the care home and to track their movements over the course of the study.

Activity Data

The smart devices use accelerometers to capture movement data, which can be processed to produce estimates of resident’s activity levels. Combined with the location data, it is possible to determine when and where residents were most active during the course of the study.

Social Interactions

The smart devices are able to detect signals from other smart devices (carried by other participants) and other transmitters that were worn by visitors and care staff. The signal strength data from these devices can be used to estimate when social interactions may have taken place.

Home Data

In addition to collecting data on the residents, it is important to also collect data about the environment, as this may provide extra insight into the data. The user data could, for example, tell us not only that a particular room is being underutilised, but the environment data could offer an explanation as to why (i.e. the room is too cold). 

The following environmental data was also collected during each study:

Ambient Noise 

Microphones on each device are used to measure the level of ambient noise (in decibels) in the vicinity of the user. This data can be used to measure how much noise the participant was exposed to over the course of the day, and to calculate/visualise the average noise levels in each room/zone.


Each beacon is fitted with a temperature sensor, which allows us to determine the temperature in different parts of the care home and to estimate the average temperature in each room/zone.

A team of computer scientists from the BESiDE team have developed an automated pipeline to process the quantitative location data collected from multiple studies of older adults in residential care. This included the use of the e-Science Central platform and the development of a range of visualisation tools that were designed to help researchers extract value from the collected data.

Processing the Data (e-Science Central)

The data from the smart devices is uploaded to e-Science Central, a cloud-based data analytics platform developed by the Digital Institute at Newcastle University. 

When a study data file is uploaded, e-Science Central can automatically start processing the data, without requiring any input from the research team. The data is processed via a series of workflows, which are constructed using workflow blocks via an online graphical user interface. The BESiDE pipeline uses many pre-existing blocks for manipulating files and data that are included with the platform, plus some bespoke BESiDE data processing blocks which were written using Java and C++.

The processed data is stored in a data warehouse, which can then be used to produce visualisations and reports on the data.

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