CLIENT
BVN architecture
ARUP engineering
UNSW
ROLE
Tiara Dobbs – Lead Researcher – Face-to-face Interactions
Others: Annisa Rizal (UNSW and BVN), Alex Lee (UNSW, ARUP), Hank Haesler (UNSW), Ben Doherty (BVN)
TOOLS
Raspberry Pi, iOT technology – WiFi and Bluetooth, Digital fabrication and 3D printing, Jupiter notebook and grasshopper s visualisers.
Introducing face-to-face interactions
Many organisations invest heavily in finding ways to differentiate themselves
through ideas, concepts, and strategies
One way of doing this is through encouraging more face-to-face interactions amongst workers.
In a workplace, encouraging face-to-face interactions through design is one of the main goals of an open-plan hot-desk style office space, and yet the tools required to test how effective these spaces do not exist. Many companies have seen great improvement from rethinking their office spaces with impromptu collaboration in mind such as Google, Pixar, Disney and many others. The engineers and the artists are no longer seated in offices across the campus but can chat over the water cooler or use a communal kitchen during the day.
Image taken from Propmodo Decoding Realestate of the Google Headquarters Office.
Background: Redesigning of ARUP and BVN workplace.
At the time of this paper, hot-desking environments were on the rise and BVN and ARUP were in the process of following suit. But what made a good, flexible, hot-desking environment?
But what actually is a ‘Face-to-face’ interaction? How do we quantify it?
There is no accepted baseline definition for what constitutes a face-to-face interaction; therefore, definitions need to be made concerning what a face-to-face interaction is or at least how we measure the probability that an interaction occurred.
Wa
The weighted probability value associated with angle.
d
Distance between two participants.
Wd
The weight to modify probability based on distance.
m
Represents the value added to the probability if the scenario is within a
meeting room.
Wm
The weight to modify the influence this term has on the result.
G
(absolute obstructions e.g. walls) the weighted probability value associated
with if there is an obstruction for a clear line of sight).
ARUP and BVN wanted to know how people interact in their workplace
Our brief was to develop a proof-of-concept product that could collect data to allow their designers to understand how people interact with others within their workplace.
This data would be used to inform future design decisions for their own workplace and future clients.
Observational studies are…awkward?
Existing research in this area has relied heavily on the manual collection of data, which made the repetition of experiments both time-consuming and laborious.
Compounding this issue, GPS proved unreliable in indoor environments, limiting its usefulness for precise tracking.
Furthermore, many of the available technologies required expensive equipment and significant manual effort, creating barriers to scalability and widespread adoption.
What if we tried to leverage commodity Hardware to gather the data?
What we knew could work…
1. Device sends a signal to connect
2. Tracking device listens for request
3. Sniffing software measures traffic
4. Data records to database
5. Video surveillance evaluates accuracy
How to build the tracker
2x Raspberry Pi
One is used to capture WiFi signals.
SD Card
To store the data.
Bluetooth USB Adapter
Our known assumptions
1. Those not carrying a device and multiple devices
This can cause a bias in our results as they enter the space. The video camera acts as a control to evaluate the impact of the data during our experiments.
2. Discoverable mode switched off
- Their device has WiFi or Bluetooth turned off
- The device has never accessed BVN’s WiFi before
3. Environmental Complexities
- Different building materials e.g. concrete columns
- Furniture
- Appliances e.g. microwaves, fridges
- Human bodies
Queried and discover
When devices are discoverable, they can be queries using the software and hardware with the individuals device information captured.
However, the tracker couldn’t detect outgoing traffic, which assumes that a person is still in area from the moment they were detected until the end of the experiment. Sniffing software was used to collect more information about every detected device and to have some assemblance of when a individual might enter or leave but it wasn’t enough.
New approach: A wearable blue tooth beacon
How it works
1. All participants to wear beacons for experimentatio.
2. Tracking device listens for request
3. Data stored on cloud and database
4. Data translated into real-time tracking data.
THE HARDWARE
Prototype Development
Wearable Beacon Hardware
The wearable beacons were built using laser cutting technology, crimps and lace.
The Reciever Hardware
The reciever was designed using Rhino and fabricated with a combination of 3D printing and assembly products.
EXPERIMENT 1
Determine Field of View
Aim
To determine whether beacon signal strength can be used to infer the facing direction of a participant, thereby distinguishing face-to-face orientation in indoor positioning systems.
Hypothesis
If a participant is facing directly toward a receiver base station, then the beacon’s signal strength received at that station will be at its maximum. Conversely, when the participant faces away, the participant’s body will obstruct the transmission path, resulting in a lower signal strength.
Methodology
Participants: A single participant was recruited for this pilot experiment.
Apparatus and Materials
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- One wearable beacon transmitter.
- Four receiver base stations capable of measuring received signal strength (RSSI).
- Indoor room with minimal interference and controlled spacing.
- Data logging system for recording signal strength values.
Data Analysis
-
-
- Recorded RSSI values were compiled for each 15-degree increment.
- A polar plot was generated to visualize the variation in signal strength as a function of participant orientation relative to each base station.
- Peaks in RSSI values were interpreted as moments when the participant was directly facing a given base station, while troughs were taken as evidence of obstruction by the body when facing away.
-
Apparatus
Procedure
-
- The participant wore a wearable beacon device fixed securely to the chest to ensure consistent orientation relative to body position.
- The participant was positioned at the center of the room.
- Four receiver base stations were placed one meter away from the participant, positioned at the cardinal points around the participant (0°, 90°, 180°, and 270°).
- The participant rotated in 15-degree increments every 10 seconds, pausing at each angle to allow signal data to stabilize.
- At each orientation, RSSI values were recorded simultaneously from all four base stations.
Results
The results showed peeks when the participant was facing the beacon, and gullies as they turn away. This meant when the signal is the strongest, and with a certian distance, we can infer the participants is facing the reciever.
Raw Data showing bluetooth data results for the experiment.
Field of View determined
Conclusions
It was concluded that the highest density of signal clustering happened at the centre of the participant’s FOV as hypothesised. Therefore, the FOV can be identified as being where the highest received density of signal occurs.
EXPERIMENT 2
Full Office Roll Out
Aim
Evaluate whether a Bluetooth Low Energy (BLE) indoor positioning system (IPS) built with commodity hardware can reliably track employee movement across an entire office floor and generate accurate journey maps for analysis.
Hypothesis
Deploying BLE beacons with overlapping coverage across the floorplan, combined with participants wearing BLE tags, will produce location data with sufficient temporal and spatial accuracy to reconstruct meaningful journey maps (e.g., common paths, dwell areas, and handoffs between zones).
Method
Participants
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300 office staff (opt-in, consented).
-
Each participant assigned a wearable BLE beacon/tag labeled with an anonymous ID.
Apparatus & Setup
Apparatus & Setup
-
Commodity BLE beacons (mains or battery powered) installed on walls/ceilings.
-
Receiver network: fixed gateways (or smartphones/Raspberry Pis) to scan BLE advertisements and forward RSSI + timestamps to a central server.
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Beacon layout: grid/zone placement to ensure full coverage with ~20–30% overlap; placement density derived from vendor-stated radius and in-situ RSSI tests (aim for ≥-70 dBm at zone edges).
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Time sync across gateways (NTP).
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Backend: ingestion service, database, and processing pipeline (RSSI smoothing, multi-gateway fusion, zone inference).
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Visualization: journey-map tool to render paths, dwell heatmaps, and transitions.
Deployment
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Distribute wearable tags to 300 participants; verify battery and ID mapping.
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Run system during standard work hours for 30 consecutive days (e.g., 20 business days).
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Gateways continuously scan and stream data; backend logs per-tag RSSI/time across gateways.
Data Processing
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Clean data (remove outliers, duplicate packets, clock drifts).
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Apply smoothing (e.g., exponential moving average) and multi-gateway fusion (e.g., strongest-signal or weighted centroid).
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Infer zone transitions and approximate paths; compute dwell time per zone and transition matrices.
Generate individual and aggregate journey maps.
Evaluation Metrics
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Coverage: % of floor area with usable signal; % time per participant with valid location.
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Accuracy: zone-level accuracy vs. ground truth spot checks; median transition detection delay.
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Stability: packet loss rate, gateway uptime, tag battery life.
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Utility: ability of journey maps to reveal high-traffic corridors and bottleneck zones (qualitative review with facilities/ops).
Ethics & Privacy
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Informed consent, anonymization, opt-out at any time.
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Aggregate reporting; no individual performance tracking.
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Data retention and access controls defined prior to rollout.
Expected Outcome
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Zone-level accuracy sufficient to produce clear journey maps (heatmaps and Sankey-style transitions), enabling insights into space utilization, collaboration hotspots, and congestion areas. Potential limitations include multipath interference and transient RSSI drops near dense metal/glass; these are mitigated by overlap, calibration, and filtering.
Results
Based on the results, we were able to determine a baseline face-to-face interaction heat map.
Detected signals for Participant ‘297’ for 1 minute.
High contrast signal visualisation for Participant 297.
The signals provided an approximate location of the participant at each point in time. Using these points, we can construct a ‘walkable’ area that represents the most likely path a participant would take around obstacles, filling in the gaps where their exact position was uncertain. This approach allows us to generate a visualisation of each participant’s movement within the floor plan.
Visualised Journey
Since we can determin best guest field of view, the location and distance each participant is from one another, we can calculate a probability of interactions within a floor plan.
The probability value represents a colour given to cell representing a ‘state’. The higher the number of interactions in relation to the total number of interactions, the higher the percentage and the redder the colour.
Up next…
TetraBin
CONTACT
Copyright © 2024 Tiara Dobbs. All rights reserved.
Based in Sydney Australia.