FieldProof: An Attention-and-Load Audit for AR Work Overlays
A kit that measures how much workers actually look at digital overlays to see if they are distracting.
An evaluation kit that instruments AR headsets during work pilots to measure which in-situ data overlays workers actually look at, when, and at what cognitive cost — giving companies behavioral evidence before scaling a deployment.
Concept
A pilot-stage evaluation kit for AR work-assistance deployments: an SDK plus analysis service that instruments AR headsets during one-to-two-week field pilots to passively capture which in-situ data overlays workers actually look at, when in the task they look, dwell time on overlay versus work object, and eye-movement-based cognitive-load proxies. It then delivers a report showing which overlays were attended, ignored, or caused load spikes, with recommendations mapped to the five situatedness lenses (for example, data surfaced at the wrong moment in the activity). The single wedge: replace post-pilot satisfaction surveys with behavioral evidence of whether the situated visualization actually worked for its target audience, before an enterprise commits to fleet-wide rollout.
Why now
The new survey [0] of 44 situated-visualization papers explicitly identifies stronger engagement with target audiences as an open future direction and shows the field has been technology-centric — meaning the actual workers are rarely the ones validating whether in-situ overlays help. The measurement machinery is already established in the corpus: attention-aware visualization [2] demonstrates tracking which embedded visualization a user is looking at and feeding that back to the system, and eye-tracking research [6] provides a model linking eye movements to cognitive load — while cautioning that HCI characteristics must be embedded in the model for accuracy, exactly what a field-hardened audit kit would encode. With enterprise AR headsets increasingly shipping integrated eye tracking, the marginal cost of running such audits alongside standard pilots is now low.
Written by GLM · 8 Oct 2026
AI assessment
A high-utility B2B audit tool that converts theoretical cognitive load and attention models into a practical validation kit for industrial AR pilots.
- Evidence strength 3/5
- The idea synthesizes three distinct research threads, but the core cognitive load model [1] is explicitly described as a theoretical position paper that has not yet been empirically validated.
- Market pull 4/5
- Enterprise AR deployments are expensive and prone to failure; providing behavioral evidence to justify fleet-wide rollouts creates a strong value proposition for industrial buyers.
- Novelty & moat 4/5
- While it shares a conceptual lineage with 'Situate' and 'Attention-Driven Data Audit,' it moves from design-time (canvas) or analyst-time (review) to field-time (pilot validation), filling a clear gap in the workflow.
- Feasibility 4/5
- Modern enterprise headsets (e.g., HoloLens 2, Magic Leap 2) already have integrated eye-tracking, making the data collection layer a matter of SDK integration rather than hardware R&D.
- Wedge clarity 5/5
- The wedge is exceptionally sharp: replacing subjective post-pilot satisfaction surveys with objective behavioral data during the 1-2 week pilot phase.
- Simplicity / focus 5/5
- The product is a focused evaluation kit (SDK + Report) with a single purpose, avoiding the trap of becoming a general-purpose AR platform.
Scored by Gemma 4 · 8 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.
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Who benefits
- TeamViewercompany
Its Frontline AR pilots with logistics and manufacturing customers need go/no-go evidence; behavioral audits de-risk enterprise-wide rollouts and differentiate its deployment methodology.
- DHL Supply Chaincompany
Having run AR vision-picking pilots, DHL needs to know whether overlays genuinely guide pickers or distract them; gaze-and-load audits turn pilot anecdotes into scale decisions.
- Tobiicompany
As an eye-tracking technology provider, an industrial field-audit product built on its sensors opens an enterprise evaluation market beyond gaming and research.
- Lockheed Martincompany
Its AR assembly and maintenance programs would benefit from pre-scale evidence that in-situ instructions are attended to and not cognitively overloading technicians.
Research it builds on
- Measuring Cognitive Load using Eye Tracking Technology in Visual ComputingJohannes Zagermann, Ulrike Pfeil, Harald Reiterer · 2016 · 222 citationsAll ideas from this paper →
- What's the Situation with Situated Visualization? A Survey and Perspectives on SituatednessNathalie Bressa, Henrik Korsgaard, Aurélien Tabard et al. · 2021 · 125 citationsAll ideas from this paper →
- Attention-Aware Visualization: Tracking and Responding to User Perception Over TimeArvind Srinivasan, Johannes Ellemose, Peter Butcher et al. · 2024 · 9 citationsAll ideas from this paper →