Introduction
Continuous monitoring is transforming healthcare. Over the past decade, technologies such as Continuous Glucose Monitoring (CGM) have demonstrated the value of collecting physiological data continuously rather than relying on occasional point-in-time measurements.
However, the future of biosensing is not simply about collecting more measurements. The next generation of wearable and implantable biosensors will need to provide context, enable cause-and-effect analysis, and support AI-driven interpretations of human physiology.
As wearable devices, smart rings, smart watches, smart glasses, and implantable sensors become increasingly connected, a new paradigm is emerging: multimodal sensing coupled with physiological AI.
Continuous Monitoring vs Snapshot Measurements
For many years, point-of-care diagnostics have relied on discrete measurements. Blood glucose testing using finger-prick strips is perhaps the most familiar example.
Even a highly diligent user testing four times per day only captures four moments from a biological system that is changing continuously across 86,400 seconds every day. This creates significant data gaps and makes it difficult to understand physiological dynamics.
Continuous monitoring changes this entirely by providing a stream of data rather than isolated snapshots.
Why Continuous Data Matters
Continuous monitoring enables:
- Identification of trends rather than individual points
- Observation of circadian and behavioural patterns
- More complete physiological datasets
- Greater confidence in interpretation
- Improved opportunities for AI-driven analysis
The success of CGM technology has already demonstrated that continuous visibility into physiological data can improve health outcomes, particularly for individuals managing diabetes.
The Banana Experiment and the Problem with Single Measurements
A simple glucose demonstration highlights an important limitation of discrete testing.
When a glucose measurement was taken after handling banana residue, the reading appeared dramatically elevated. The apparent increase was not due to a rapid physiological response but rather contamination of the sample.
This illustrates a key challenge:
A single measurement can be wrong.
Whether caused by:
- User error
- Sample contamination
- Poor technique
- Environmental factors
An incorrect snapshot can mislead both people and algorithms.
Continuous monitoring systems are inherently more resilient because anomalous readings can be identified and filtered against the broader trend.
Lessons from Continuous Monitoring
✅ Trends are usually more informative than individual measurements.
✅ Outliers can be detected more easily.
✅ Algorithms can distinguish genuine events from artefacts.
✅ The overall dataset becomes more robust.
From Physical AI to Physiological AI
One of the most interesting developments in artificial intelligence is the growing focus on understanding cause and effect.
Researchers are increasingly investigating physical AI systems capable of learning how objects interact with the physical world. A common example is predicting the behaviour of a bouncing ball and understanding the forces that govern its motion.
Healthcare presents a comparable but more complex challenge.
Instead of modelling physics, biosensor developers are interested in modelling physiology.
Examples include:
- Food causing glucose responses
- Exercise altering metabolism
- Sleep affecting recovery
- Stress influencing hormone levels
- Circadian rhythms changing biomarker concentrations
The challenge is not simply detecting physiological changes but understanding the underlying causes.
This is where continuous monitoring combined with AI becomes especially valuable.
Why AI Needs Context
A biosensor can tell us what happened.
It cannot always tell us why it happened.
For example, a glucose rise could be caused by:
- Breakfast
- Lunch
- Exercise
- Illness
- Psychological stress
- Sleep disruption
Without context, physiological data remains incomplete.
This represents one of the fundamental challenges in biosensor development.
To understand cause and effect, AI systems need additional information about the user's environment and behaviour.
The Rise of Multimodal Sensing
Context comes from additional sources of information.
This is increasingly driving interest in multimodal sensing, where multiple sensor streams are analysed together.
Examples include:
| Device | Information Provided |
|---|---|
| Continuous Glucose Monitor | Glucose concentration and trends |
| Smart Watch | Heart rate, activity and exertion |
| Smart Ring | Sleep and recovery metrics |
| Smart Glasses | Food intake and environmental observations |
| Implantable Sensor | Long-term physiological monitoring |
By combining these independent streams, it becomes possible to establish relationships between behaviour and physiological outcomes.
For example:
Meal → Digestion → Glucose Response
or
Exercise → Increased Energy Demand → Glucose Change
This allows AI systems to begin developing meaningful physiological models.
What the Market Is Telling Us
Many of the world's largest health technology companies are moving toward multimodal, AI-enabled health platforms.
Industry developments include:
- Abbott's continued investment in CGM technology
- Dexcom partnerships and ecosystem expansion
- Oura smart ring integrations
- Whoop health monitoring platforms
- Apple's long-running work on glucose sensing
- Samsung's interest in non-invasive monitoring
- Google AI integrations for health data interpretation
These developments all point in the same direction.
The future is not a single sensor.
The future is a connected physiological ecosystem where multiple devices contribute context and understanding.
Screen Printed Electrodes: The Foundation of Biosensor Development
Many commercial biosensors begin life as screen printed electrode (SPE) systems.
These technologies continue to play a vital role in:
- Assay development
- Electrochemical research
- Sensor optimisation
- Wearable biosensor development
- Manufacturing scale-up
Relevant Resources
🔬 Screen Printed Electrodes
https://shop.zimmerpeacock.com/en-gb/collections/bare-electrodes
🔬 Electrode Solutions & Accessories
https://shop.zimmerpeacock.com/en-gb/collections/solutions
Commercial systems such as glucose monitoring technologies may ultimately become highly refined products, but development often begins with the same electrochemical foundations available to researchers today.
Develop Before You Productize
A recurring challenge in biosensor innovation is the desire to move directly to a custom product without first fully validating the science.
The most successful programmes typically follow a staged approach.
| Stage | Primary Goal |
|---|---|
| Research | Validate sensing chemistry |
| Development | Optimise sensor performance |
| Prototyping | Create reproducible devices |
| Pilot Manufacturing | Improve scalability |
| Product Development | Build deployable systems |
| Commercialisation | Regulatory approval and launch |
Attempting to jump directly to a polished product often leads to unnecessary cost and development risk.
Practical Development Tools
⚙️ Biosensor Single Purpose Board
https://shop.zimmerpeacock.com/en-gb/products/biosensor-single-purpose-board
⚙️ Biosensor Development Platform
https://shop.zimmerpeacock.com/en-gb/pages/sensors
These tools support early-stage electrochemical development and biosensor validation before investment in custom hardware.
The Reality of Wearable Biosensor Development
Wearable biosensors are often perceived as fast-moving startup opportunities.
In reality, successful products require:
- Significant financial investment
- Specialist interdisciplinary teams
- Years of engineering effort
- Clinical validation
- Regulatory expertise
- Manufacturing excellence
The journey from concept to approved medical device is typically measured in years and often requires substantial funding.
When evaluating new wearable biosensor ventures, useful questions include:
- How long has the technology been under development?
- What validation data exists?
- What level of funding has been secured?
- What technical milestones have been achieved?
- Has the science been demonstrated at scale?
These indicators provide a much clearer assessment of maturity than marketing imagery or conceptual renderings.
Learning from Commercial CGM Development
Modern CGM systems provide valuable lessons for anyone developing biosensors.
The sophisticated devices available today are the result of decades of progress in:
- Electrochemistry
- Materials science
- Manufacturing methods
- Biomedical engineering
- Clinical research
Importantly, many commercial sensor architectures share roots in technologies familiar to electrochemical researchers, particularly printed electrode systems and precision deposition processes.
This demonstrates that advanced commercial devices often start with surprisingly simple development tools.
Beyond Wearables: The Move Toward Implantable Sensors
While wearables remain highly important, implantable biosensors represent the next major frontier.
Potential advantages include:
- Continuous physiological access
- Reduced user interaction
- Long-term monitoring capability
- Improved patient compliance
- Richer biological datasets
Development is a gradual process typically involving:
- Laboratory validation
- Sensor optimisation
- Small-animal studies
- Large-animal studies
- Human clinical development
- Regulatory approval
- Commercial deployment
Programmes may take years to mature, but implantable sensing offers the possibility of monitoring biomarkers continuously and unobtrusively.
Continuous Data Enables Better Physiological Understanding
Whether measuring:
- Glucose
- Lactate
- Hormones
- Electrolytes
- Other small molecules
Continuous sensing generates datasets that are fundamentally different from traditional testing approaches.
As AI tools improve, the greatest value may not come from measuring biomarkers themselves but from understanding the relationships between behaviour, physiology and health outcomes.
This requires:
- Continuous data
- Contextual information
- Multimodal sensing
- Physiological models
Together, these elements create a foundation for the next generation of healthcare technologies.
Practical Takeaways
✅ Key Insights
- Continuous monitoring provides significantly more information than snapshot testing.
- AI systems require context to understand physiological changes.
- Multimodal sensing is becoming increasingly important across the health technology sector.
- Screen printed electrodes remain a foundational technology for biosensor development.
- Development should focus on validating science before custom product design.
- Wearable biosensors require substantial investment, expertise and time.
- Implantable sensors represent an important future direction for continuous monitoring.
💡 What This Means for Developers
- Focus on collecting high-quality continuous datasets.
- Design systems that capture contextual information.
- Combine multiple sensing modalities wherever possible.
- Build physiological understanding, not just measurement capability.
- Consider manufacturability and scalability early in development.
- Use proven development platforms before investing in custom architectures.
Looking Ahead
The future of biosensing is evolving beyond simple measurement.
The next generation of healthcare systems will increasingly combine:
- Continuous monitoring
- Artificial intelligence
- Physiological modelling
- Multimodal sensing
- Wearable biosensors
- Implantable biosensors
Success will depend not only on detecting biological changes but on understanding their causes.
The organisations that can combine sensing, context and AI-driven interpretation will be best positioned to unlock the next wave of innovation in healthcare monitoring.
Continuing the Conversation
The move from wearable sensing to implantable monitoring is creating new opportunities across diagnostics, digital health, personalised medicine and biomedical engineering.
Whether you are developing a novel biosensor, exploring electrochemical technologies, or evaluating continuous monitoring applications, collaborative discussion often accelerates innovation.
To explore the topic further or discuss your biosensor development programme, visit:
https://www.zimmerpeacock.com/contact
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