Wearable IoT Technologies

🏥 Two #wearable companies. Combined valuation: over $20 billion. And we're just getting started. WHOOP has just raised $575 million in a Series G round at a $10.1 billion valuation. What is especially notable is not only the size of the round, but the signal behind it: investors include Abbott and Mayo Clinic. That suggests wearables are increasingly being seen not merely as consumer wellness products, but as strategically relevant assets in the future of healthcare. Meanwhile, ŌURA has been reported at roughly an $11 billion valuation, reinforcing the scale of market confidence in continuous, consumer-facing health monitoring. What makes this shift important is not just the hardware. It is the growing clinical relevance of continuous, real-world data. Recent literature shows that wearable technologies are moving beyond lifestyle tracking into more serious remote monitoring use cases. A new Nature Portfolio study demonstrated that #smartwatch-based monitoring can support the remote assessment of heart failure patients using continuous physiologic and behavioral data. A JMIR mHealth and uHealth systematic review further showed that wearables are increasingly used for chronic disease monitoring, especially in cardiovascular and neurological applications. At the same time, the real acceleration comes from analytics. As #AI-enabled interpretation improves, wearable data is becoming more actionable: not just raw signals, but contextualized information about recovery, stress, rhythm, activity, and deterioration risk. A JMIR systematic review on AI-enabled medical devices highlights wearable monitoring as one of the domains where AI is enabling more continuous, #personalized health management. This is why wearables are becoming strategically relevant beyond consumer tech. They are helping to push healthcare away from a model that mainly reacts to illness, and toward one that increasingly supports prevention, early detection, and continuous management. A recent European Heart Journal – Digital Health review describes wearable technologies as part of a transformation in cardiovascular care through continuous monitoring outside traditional clinical settings, while also making clear that large-scale impact still depends on validation, workflow integration, and governance. For those of us working in healthcare IT, the key question is no longer whether wearable-generated data will matter. The real question is: Are our health IT systems ready to receive, contextualize, and operationalize this data? #DigitalHealth #Wearables #RemotePatientMonitoring #PreventiveCare #AIinHealthcare #HealthcareIT #Interoperability #DigitalTransformation #Virgobit

University of Technology Sydney researchers just published findings that reframe how we think about continuous health monitoring. Their team, led by analytical chemist Dr. Dayanne Mozaner Bordin and biomedical researcher Dr. Janice McCauley, demonstrates that AI-powered sweat sensors can continuously track hormones, medication levels, and early warning signals for diseases like diabetes, Parkinson's and Alzheimer's - without blood draws, without timing, purely from a skin patch that collects and decodes your sweat in real time. This matters because it fills a critical gap in how we currently approach disease prevention. Today, we rely on episodic blood tests and patient-reported symptoms. Sweat sensors paired with AI change that equation entirely. They correct the bias toward acute, symptomatic diagnosis and open the door to longitudinal, biochemical understanding of how bodies degrade before we notice. The research, published in the Journal of Pharmaceutical Analysis, shows that by measuring multiple biomarkers simultaneously and transmitting data wirelessly, we can identify physiological drift toward chronic disease months before clinical symptoms emerge. Why does this matter beyond academia? Because it demonstrates that AI can extract clinically actionable intelligence from real-world, continuous physiological data. Wearables are no longer just tracking steps or heart rate. They're becoming diagnostic instruments, generating the kind of continuous biochemistry that clinicians have always wanted but never had access to outside a lab environment. I’ve written about this extensively on LinkedIn, but my followers know I’m a strong advocate for wearables. This is exactly the direction I hope our healthcare systems are heading: wearables and sensor-rich environments as complementary infrastructure, continuously feeding risk models, decision support tools, and personalized care pathways. Not replacing clinicians or traditional diagnostics, but augmenting them with a much richer, longitudinal picture of health. The UTS research corroborates our market observations at Monterail: wearables have transcended their initial focus on wellness and have evolved into essential components of preventive medicine infrastructure. Dr. Dayanne Mozaner Bordin and Dr. Janice McCauley at University of Technology Sydney - this work deserves wider attention in the digital health builder community. Who else is integrating sweat or other novel biomarker streams into care platforms?

China just bent the rules of electronics — literally. Facinating? Chinese and global researchers are advancing Metal-Polymer Conductors (MPCs) — circuits made from liquid metals like gallium–indium embedded in elastic polymers — that defy traditional rigid wiring by remaining conductive even when stretched up to 500% or more. Why this is a big deal: 🔹 High Stretchability: Certain liquid-metal conductors maintain electrical conductivity even when stretched 5× their original length. 🔹 Durability: Printable metal-polymer conductors can withstand over 10,000 cycles of stretching with minimal resistance change (<3%). 🔹 Conductivity: Hybrid conductors based on indium alloys can achieve extremely high conductivity (~2.98 × 10⁶ S/m) with minimal resistance change under extreme strain. 🔹 Fine Feature Sizes: Advanced techniques can pattern circuits as small as 5 micrometers, rivaling conventional PCBs. Market Insight: The global market for wearable and flexible devices is expected to surge into the hundreds of billions of dollars, with advanced stretchable materials at the core of the next wave of innovation. (Wearable tech projected >US$150B by 2026 in soft electronics growth — wearable industry data) Where AI Fits In: AI is not just hype — it’s accelerating how we design and discover materials like MPCs. AI/ML models help predict material properties — like conductivity and mechanical resilience — before physical prototypes are made. Computational simulations can evaluate thousands of polymer + metal combinations far faster than physical testing alone. AI-assisted optimization reduces lab iterations, cutting time and cost in early-stage development. In other words: AI + materials science = faster discovery of smarter, stretchable electronics. Potential Applications: Soft robotics that mimic human motion Wearables that feel like fabric Artificial skin with embedded sensing Health monitoring devices that conform to the body On-skin motion recognition and bioelectronics. The era of electronics you can twist, stretch, and wear is here — and AI is helping make it a reality. #FlexibleElectronics #MaterialsScience #AIinInnovation #SoftRobotics #WearableTech #DeepTech #FutureOfElectronics #Innovation

7 wearable and sensor innovations pushing health beyond “wellness” tracking this month: 🔘 Sibel Health is developing an AI-enabled wearable that tracks scratching behaviour in people with atopic dermatitis, turning something usually seen as a subjective symptom into a measurable clinical signal that could also support drug development. 🔘 CranioSense is working on a non-invasive approach to measuring intracranial pressure, which today often requires invasive procedures, and if validated could make brain pressure monitoring safer and more continuous in routine clinical care. 🔘 University of Technology Sydney researchers are developing AI-powered sweat sensors that can decode body chemistry in real time, tracking hormones, medication levels and potential early warning signs of disease, potentially offering a non-invasive alternative to some forms of blood testing 🔘 ŌURA rings are being used within Medicare Advantage Plans, with around one-third of eligible members opting in and sharing biometric data, which is already leading to improvements in sleep and light activity and is paving the way for deeper clinical use cases such as hypertension monitoring 🔘 Samsung Electronics is preparing to launch an AI Brain Health tool that uses data from smartphones and wearables, including speech, movement and sleep behaviour, to help detect early signs of dementia while aiming to keep the experience privacy-aware and clinically relevant 🔘 Researchers at the University of Arizona have created a wearable mesh sleeve that monitors gait and subtle movement patterns to identify early signs of frailty in older adults, with the goal of shifting care from reacting after a fall to proactively supporting prevention through continuous remote monitoring 🔘 And China is testing “smart urinals” that analyse urine in real time for markers like glucose and protein, which opens up interesting conversations about passive health screening, consent, and how health data might be gathered in everyday environments. 💬We are steadily moving from episodic health snapshots to passive, continuous and contextual signals across movement, sleep, behaviour and even body chemistry. The technology is getting closer. Now the real work is around validation, governance, reimbursement and making sure the data actually makes a difference in peoples lives 👇 Links to articles in comments #DigitalHealth #Wearables #AI

Google just revealed SensorLM, an AI that learns the "language" of activity sensor data. Instead of just showing you numbers, it can tell the difference between a "light swim" and "strength workout" from sensor data alone - and generate human-readable descriptions of your activities. But here's what they're not telling you about building contextual AI for IoT. When teams see demos like this: ➞ Leadership gets excited about adding "contextual intelligence" to existing products without understanding the data requirements ➞ Engineering teams underestimate the gap between Google's controlled dataset and messy real-world sensor streams ➞ Battery life becomes the hidden constraint - running sophisticated AI models on-device drains power faster than users expect ➞ Edge processing limitations force compromises that nobody planned for in the initial excitement We've seen this pattern repeatedly in IoT projects. The demo works beautifully. The production reality is much harder. Our work with SpotOn taught us that even basic sensor optimization - getting GPS, cellular, and Bluetooth to work together efficiently - requires significant cross-discipline engineering effort. Adding contextual AI on top of that? You're looking at a completely different level of complexity. But here's what gets me excited: we worked with a wearables healthtech innovator five plus years back on IoT patient monitoring. This kind of contextual data analysis simply wasn't realistic then. Now imagine a world where a doctor gets a dynamic 24-hour analysis of a patient's heart rate data - not just numbers, but "elevated during phone calls with family" or "spiked during physical therapy sessions." Or AI that recognizes patterns in biomarker data and sends intelligent alerts: "Patient's stress indicators suggest anxiety, not cardiac issues." SensorLM represents genuine progress that could be transformative. The teams that will win are staying on top of these advances while being realistic about constraints. What's your experience adding AI to IoT products? ♻️ Repost if you liked it ➕ Follow me, Nick Tudor, for more IoT and AI Insights

Wearables are not limited by what they can measure. They are limited by what happens after the signal appears. The wearable industry has solved sensing. We can now capture continuous biometric data with remarkable precision. Heart rate variability. Temperature deviation. Sleep disruption. Motion patterns tracked over months and years. The signals are constant, passive, and increasingly reliable. But most of that data still stops short of impact. Users see dashboards. Clinicians receive raw metrics. Systems are left to interpret meaning without direction. In a recent Euronews interview, ŌURA's CEO described a future where wearables move beyond periodic tracking toward continuous, predictive health insight. That direction is right. But prediction alone does not change outcomes if nothing happens next. Tracking health is not the same as improving health and safety. The real limitation of wearables today is not sensor quality or form factor. It is the absence of an infrastructure layer that turns detection into action. This is where the next phase of wearable technology begins. LifeKnight is not a device company. We are an infrastructure company built for response. Our platform sits beneath wearables and health sensors, ingesting biometric data, contextualizing it over time, applying AI agents to detect meaningful risk, and routing outcomes to real-world response systems when action is required. This is what transforms wearables from passive observers into active participants in health and safety. Better sensors will continue to emerge. Rings, patches, bands, and devices we have not yet imagined. But the winners in this space will not be defined by who measures more. They will be defined by who closes the loop between signal and response. Actionable technology is the inflection point. #LifeKnight #WearableTech #HealthTech #ActionableAI #AIinHealthcare #DigitalHealth #ConnectedHealth #PredictiveHealth #HealthInfrastructure

Your watch might just save your life. For seniors, it already is. I'm talking about how wearables and health monitoring tech are transforming senior care. Here's how it's happening: 1. Real-time monitoring Smartwatches, sensor-equipped clothing, and biometric patches now track: • Heart rate • Blood pressure • Oxygen saturation • Glucose levels ➡️ Abnormality? The system flags it. ➡️ Risk detected? Intervention is immediate. ➡️ Outcome? Fewer emergencies, better care. 2. Telehealth Integration Wearables now sync directly with telehealth platforms, giving virtual care more power. Doctors and nurses can: • See real-time data • Adjust treatment plans • Reduce unnecessary visits This bridges distance and ensures care doesn’t wait. 3. Personalized & preventive care Thanks to AI and machine learning, wearables can now: • Detect patterns • Predict health risks • Personalize interventions We're moving from “reactive care” to preventive, predictive, and proactive care. Here's how I see it: This isn’t just tech... It’s compassion in action. So let’s keep building a future where seniors thrive, safely and independently. If you’re innovating in this space, I’d love to connect.

𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: 𝐀𝐈 𝐚𝐧𝐝 𝐈𝐨𝐓 𝐀𝐫𝐞 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐅𝐢𝐱𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞'𝐬 𝐁𝐢𝐠𝐠𝐞𝐬𝐭 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 Last week, I watched my neighbor's cardiologist catch his heart failure risk from his Apple Watch data. Not fiction. Real medicine happening right now. Here's what's actually working in hospitals today: 𝐌𝐚𝐲𝐨 𝐂𝐥𝐢𝐧𝐢𝐜'𝐬 𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐫 Mayo Clinic developed an AI system that reads regular ECGs and spots silent heart disease that doctors typically miss. The AI identifies patients with weak heart pumps who "would have slipped through the cracks." They're using this on thousands of patients with remarkable accuracy. 𝐓𝐡𝐞 𝐀𝐩𝐩𝐥𝐞 𝐖𝐚𝐭𝐜𝐡 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 Mayo researchers created an AI algorithm that analyzes Apple Watch ECG data to detect weak heart pumps. Your smartwatch isn't just tracking workouts anymore - it's performing cardiac screening that used to require expensive hospital equipment. 𝐑𝐞𝐚𝐥 𝐍𝐮𝐦𝐛𝐞𝐫𝐬 𝐟𝐫𝐨𝐦 𝐑𝐞𝐚𝐥 𝐇𝐨𝐬𝐩𝐢𝐭𝐚𝐥𝐬 A study in JAMA showed AI lung cancer detection hit 94% accuracy versus radiologists at 65%. That's not marginal improvement - that's life-saving difference. Recent research shows "significant improvements in patient outcomes, including enhanced glycemic control in diabetes management, early detection of cardiovascular anomalies, and a reduction in hospital admissions for chronic disease patients through AI-enabled remote monitoring." 𝐓𝐡𝐞 𝐒𝐭𝐮𝐟𝐟 𝐍𝐨𝐛𝐨𝐝𝐲 𝐓𝐚𝐥𝐤𝐬 𝐀𝐛𝐨𝐮𝐭 𝐑𝐞𝐦𝐨𝐭𝐞 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Diabetes patients now have continuous glucose monitors that alert doctors before dangerous spikes happen. No more surprise ER visits. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Hospitals can predict which patients will deteriorate 6-12 hours before traditional methods. ICU teams get early warnings instead of emergency calls. 𝐀𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐯𝐞 𝐑𝐞𝐥𝐢𝐞𝐟: AI applications to remote patient monitoring through "intelligent telehealth through wearables/sensors" are reducing doctor burnout by handling routine monitoring tasks. 𝐖𝐡𝐚𝐭 𝐓𝐡𝐢𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐞𝐚𝐧𝐬 Your doctor isn't drowning in paperwork anymore because AI handles data analysis. Your chronic condition gets monitored 24/7 without you thinking about it. Life-threatening conditions get caught early, when they're treatable. 𝐓𝐡𝐞 𝐇𝐨𝐧𝐞𝐬𝐭 𝐓𝐫𝐮𝐭𝐡 This isn't about robots replacing doctors. It's about giving doctors superpowers - letting them focus on healing while technology handles the heavy lifting. The transformation is quiet, steady, and happening in every major healthcare system. Not flashy headlines, just better outcomes. Have you noticed these changes in your healthcare? What's your experience been? #HealthcareAI #MedicalTechnology #PatientCare #DigitalHealth

A healthcare company was struggling with low patient compliance and poor communication between providers and patients—leading to suboptimal outcomes and regulatory concerns. How wearable tech is changing remote care: By integrating wearable devices into their Remote Patient Monitoring (RPM) programs, they enabled continuous, real-time collection of patient data—such as heart rate, blood pressure, and glucose levels—directly from patients’ homes. This data was securely transmitted to healthcare professionals, allowing for timely interventions and personalized care plans. Results: - Improved patient compliance with treatment and monitoring plans through reminders and real-time feedback - Reduced hospital readmissions and in-person visits due to early detection and proactive management - Enhanced patient engagement and satisfaction by empowering individuals to take a more active role in their health Real change happens when technology meets strategy. Would this solution work for your organization? #AIinHealthcare #HealthTech #DigitalHealth

𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐖𝐞𝐚𝐫𝐚𝐛𝐥𝐞𝐬: 𝐂𝐨𝐧𝐪𝐮𝐞𝐫𝐢𝐧𝐠 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐟𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩. Wearable technology is reshaping industries like healthcare, fitness, and productivity, offering real-time insights that drive decisions and enhance user experiences. Yet, latency in data processing remains a critical challenge. Delays in transmitting and analysing data can compromise outcomes, from life-saving health alerts to real-time engagement, directly impacting trust and market credibility. The solution lies in strategic investments. Edge computing enables faster, localized processing, reducing dependency on the cloud. Incorporating 5G networks and AI-powered analytics further ensures real-time performance and reliability under demanding conditions. Overcoming latency isn’t just a technical hurdle—it’s a strategic move to deliver consistent value, build trust, and establish market leadership. Solving this challenge will separate innovators from the rest, turning potential into sustainable success. #WearableTechInnovation #RealTimehealthcareData #EdgeComputingSolutions #AIDrivenHealth