BioSignal Metrology: A Framework Connecting R&D, Validation, and Business Decision Optimization
Overview
Wearable biosensor companies, semiconductor innovators, and digital health organizations faced a critical strategic question: could highly integrated optical sensor technologies deliver the signal quality required for reliable health, wellness, and medical-grade applications?
What objective classification categories were needed?
Classical standards, such as the IEC 60601 group, were not sufficient to guide developers in the right direction.
This is why we developed and applied the BioSignal Metrology methodology to answer this question quantitatively.
The method made it possible to evaluate wearable (and generally non-invasive) biosensor performance at the raw signal level, before committing to costly product-development, algorithm-development, clinical-validation, or regulatory pathways.
By establishing a statistically grounded validation framework for pure biosignal quality, DataSenseLabs enabled decision makers to identify the true competence limits of wearable biosensors and make evidence-based technology, investment, and product strategy decisions with reduced risk.
The Challenge
The wearable health market was moving rapidly from fitness and wellness tracking toward more advanced health-informatics, R&D purpose and medical-monitoring applications.
For executive teams, this created a high-risk decision environment. Miniaturized and integrated sensor platforms offered clear business advantages: smaller form factor, shorter development cycles, lower system complexity, and faster route to scalable products. However, these advantages were only valuable if the integrated biosensor could preserve the physiological signal quality required for reliable downstream analytics.
At the time, there was a major validation gap. Engineering-level component tests could describe the hardware. Clinical studies could evaluate final outcomes. But neither provided a practical, quantitative answer to a key business question:
Is the raw biosignal good enough to support the intended use case?
Without this answer, organizations risked making high-cost decisions based on incomplete evidence. They could select the wrong sensor architecture, overinvest in algorithm development, enter clinical validation too early, or move toward regulatory discussions without understanding the real signal-level limitations of the technology.
This gap created several strategic barriers:
Although today FDA- and EU-level guidance, standards, and expectations for digital health and wearable technologies are more developed, biosignal-level metrology remains a specialized niche. The ability to quantify data-level, signal-level, and frequency-band-specific performance remains essential for reducing uncertainty in R&D and use cases specific regulatory decision-making.
What We Did
DataSenseLabs applied the BioSignalMetrology methodology to validate whether a newly developed integrated biosensor module could achieve signal-quality performance comparable to an established discrete reference system.
The methodology has been extended and applied over the years for the benefit of several projects and companies, including Bittium, PCB Design, European Space Agency–related national R&D, aviation-medicine decision protocol development, and portable sensor testing and evaluation for CBRN use cases and EU funded project design and execution (HORIZON, EIC, EURAMET, etc.).
The current case study example illustrates an analysis that compared Analog Devices’ ADPD188GGZ integrated optical module with the ADPD107 discrete optical front-end, which was treated as the proven reference platform. Instead of evaluating only final heart-rate or application-level outputs, the methodology focused on the raw photoplethysmography signal itself. This allowed the team to determine whether the new integrated architecture preserved the physiological information required for health-related use cases.
Key initiatives included:
The Solution
DataSenseLabs delivered a BioSignal Metrology-based decision framework that connected sensor engineering performance with strategic product-readiness assessment.
The framework provided a missing validation layer between component-level testing and full clinical evaluation.
This was essential because clinical or application-level results can hide the underlying cause of poor performance.
A product may fail because of the sensor, the algorithm, the mechanical design, the use case, or the data pipeline. BioSignal Metrology isolates the raw biosignal and allows decision makers to understand whether the sensor platform itself is technically capable. More importantly, it enables the independent identification of influential factors, such as the electrode or sensor surface, mechanical and ergonomic design, computational-method characteristics, and other system-level contributors to signal quality.
The methodology enabled quantitative comparison across the most relevant dimensions of biosignal quality, including waveform similarity, peak-region stability, frequency-band coherence, and phase-domain behavior.
Delivered Capabilities
Results and Business Impact
The BioSignal Metrology analysis demonstrated that the ADPD188GGZ integrated optical module could achieve signal-quality performance comparable to the ADPD107 discrete reference solution.
For decision makers, across several projects, the result was significant. It showed that optical integration could be pursued without compromising the raw physiological signal quality required for relevant wearable-health applications. This created a stronger basis for selecting integrated sensor architectures, accelerating product-development decisions, and reducing uncertainty before larger investments were made
Operational Impact
Innovation Impact
Established BioSignal Metrology as a necessary validation layer for wearable biosensor development.
Outcome
DataSenseLabs transformed wearable biosensor validation from a subjective, outcome-dependent, or hardware-only assessment into a quantitative decision-support discipline.
BioSignal Metrology made it possible to identify the real competence limits of wearable biosensors at the raw data and signal-quality level, helping organizations save time and money by reducing the risk of unestablished technical, product, and regulatory decisions.
Selected components of the methodologies and workflows developed within this initiative have been externally disseminated and validated through scientific publication and professional presentation activities.
Certain parts of the developed framework and associated methodologies were published in peer-reviewed research:
The results and implementation experience were also presented to the broader professional community at: Analog Devices Technical articles: Optical Integration Without Compromises
Application of the Biosignal Metrology Method to determine and decrease the measurement uncertainty of the oscillometric blood pressure measurement solutions: Honvédorvos (Journal of Military Medicine): Issue 2019 (71)/3-4, pp 42-51.