Future Of Work Technologies

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

If you ask most data leaders, “What drives your users to take the highest value actions in your product?”, they’ll gaze back at you with a pained look on their face. They’ll probably respond through gritted teeth, “That’s a hard question.” And they’re right. It’s not that they don't care; the opposite in fact. But it’s an incredibly complex puzzle, and they wish they had better answers. Throughout my career, understanding the drivers of high value actions has been *the* burning analytics question. At Wealthfront, we obsessed over what led customers to transfer their other investment accounts to us. At Uber, it was the factors behind frequent trips and subscription sign-ups. At Gopuff, it was what drove large orders and purchases of high-margin products. The problem is, traditional analytics tools like BI dashboards and spreadsheets can’t untangle the web of factors that lead to high value actions. Answering these questions requires high-dimensional causal factor analysis, decomposing outcomes across dozens, or hundreds, or even thousands of input variables. In other words, they require machine learning. This is what the most advanced analytics teams are doing — using ML to find the needles in the haystack and unveil unexpected relationships between behaviors and outcomes. The good news: upgrading your product analytics with ML is within reach. In our latest article, Jeremy and I break down three core techniques you can use today. The topic is on our mind because we’re coming across it frequently at Delphina. We're eager as always for feedback and reactions, and if you’re tackling a similar problem and want to brainstorm, reach out! #datascience #analytics #machinelearning #artificialintelligence

Accelerating Formula 1 Performance through Modern Data Architecture! In the high-speed world of F1, performance is not just about horsepower (hp); it's about harnessing data effectively. Would like to draw some correlation between F1 excellence and modern data architecture: 1. Real-Time Analytics Pit Stop   - F1 Teams rely on split-second decisions during pit stops. You must have seen this when race cars stop at the pit stop. Real-time telemetry helps in optimizing strategy.   - Data Architecture: Modern platforms like Apache Kafka or AWS Kinesis follow the same architecture and can ensure real-time data streaming for quick decision-making esp. in manufacturing industries. 2. Data Lakes & Storage Strategies:   - In F1, GB's of data are generated per lap. Efficient storage is crucial for post-race analysis and ongoing development.   - Just as F1 teams use data lakes, businesses can leverage enterprise platforms for scalable, cost-effective data storage what we now call it as Modern Data Platform. 3. AI & Machine Learning Precision:   - In F1, AI analyzes telemetry data to predict performance, improve strategy, and enhance car components.   - Platforms like TensorFlow or PyTorch empower businesses to implement machine learning models for predictive analytics, anomaly detection, and optimization. 4. Cloud Computing for Scale:   - In F1, teams need scalable solutions for data processing, especially during race weekends with massive data influx.   - Enterprise Cloud platforms provide the scalability needed to handle peak data loads efficiently. 5. Edge Computing at the Track:   - In F1, Telemetry data is collected at the edge (on the car) for real-time insights during the race.   - Edge computing, with platforms like Azure IoT Edge or AWS IoT Greengrass, brings real-time processing closer to data sources, reducing latency. 6. Cybersecurity in the Fast Lane:   - In F1, Data security is paramount to protect teams' proprietary technology and strategies.   - Robust cybersecurity measures, including encryption and access controls, are integral components of modern data platforms like Apache Ranger or HashiCorp Vault. In essence, Formula 1's success hinges on the seamless integration of data-driven insights, mirroring the principles of modern data architecture. As F1 pushes the boundaries of speed and precision, businesses can draw inspiration from these parallels to enhance their own performance through advanced data strategies. #Formula1 #DataArchitecture #ModernDataPlatforms #AI #Analytics #TechInnovation #data #digitaltransformation #strategy

The most important wearable of the next decade won’t be something you show. It won’t sit on your wrist. It won’t light up. You may even forget it’s there. Health technology is shifting from devices we notice to systems that quietly work in the background. Lumia™ Health is a strong signal of that shift. They didn’t choose the wrist. They chose the ear. They didn’t optimize battery life. They removed charging altogether. A solar-powered earable, under one gram, always on, fueled by ambient light as life unfolds. No habits to build. No charging reminders. No data gaps because the device died. But the real innovation isn’t convenience. It’s what becomes measurable. The wrist captures movement. Steps. Heart rate. Activity trends. The ear opens access to cephalic blood flow and that’s how blood reaches the brain in real time. That matters for symptoms people experience daily: brain fog, dizziness, fatigue, head pressure. Not acute illness. Not “nothing” either. These signals live between annual checkups and lived experience that quietly shaping focus, energy, and performance. With continuous sensing, context appears: during work, under stress, in recovery. Health stops being episodic. It becomes adaptive. Instead of reviewing data after the fact, the system responds as conditions change by detecting early shifts, linking them to behavior and environment, and guiding action before symptoms escalate. This is the next phase of wearables: less attention, more intelligence. From a health futurist’s lens, three forces are converging: • Invisible design over visible tech • Deep physiology over surface metrics • Continuous guidance over periodic insight Lumia™ Health sits right at that intersection. We’re moving beyond the wrist. Beyond dashboards. Beyond once-a-year health. Toward silent, solar, brain-aware systems that work with us, not on us. #wearabletech #thewearablesexpert How do you see earables and invisible wearables redefining health products?

T-Mobile Creates Virtual #5G Private Networks as a Service Based on its leading edge #5G Advanced Network core, T-Mobile is able to offer a wide array of services that support the critical needs of business and enterprise users, and that others may not be able to offer. T-Mobile is announcing a new feature called #Edge #Control that significantly improves the feasibility of deploying enterprise-grade private 5G solutions, while also eliminating the need to build out a company-specific private network or the need to manage it. While private 5G won’t fully replace #WiFi solutions, for many mission critical and/or information sensitive solutions private 5G offers a more inherently secure and manageable capability. It also creates a real time work environment over a wide area and/or multiple locations for mobile needs that may not be effectively enabled with WiFi or other networks. Read our complete analysis with our conclusions in the article below, but here's our Bottom Line: T-Mobile continues to offer leadership services that make organizations more effective. Its Edge Control solution offers most of the benefits of a full private 5G implementation for a fraction of the cost and minimal effort. Creating a virtual 5G private network service is a major step towards enabling more secure, more functional and more resilient corporate network capability, while also moving the potentially large CAPEX cost of a private 5G implementation to an OPEX model that is more attractive for many enterprises and smaller businesses. It also eliminates the delay in getting a 5G private network up and running, while also providing an ability to scale up or down as necessary. By creating this service, T-Mobile is expanding the market availability of private 5G and making it available for many companies that otherwise may not find it attractive. We expect many organizations to move in this direction. Organizations exploring private 5G for its benefits should evaluate the T-Mobile Edge Control solution for its many advantages over custom build outs. T-Mobile For Business #Private5G #Wireless #Cellular #Enterprise #Security #OPEX #CAPEX #Edge

⌚ What if the most powerful performance tool at work wasn’t a new app—but the watch on your wrist? I still remember when wearables were just about counting steps. 10,000 steps. Close your rings. Burn calories. Simple. But today? They’re evolving into something much bigger. Wearables now track: 🔹 Stress levels 🔹 Sleep quality 🔹 Heart rate variability 🔹 Recovery patterns 🔹 Early signs of fatigue or burnout And this is where things get interesting—especially in the workplace. I’ve seen how performance isn’t just about time management. It’s about energy management. When companies use aggregated and anonymized wearable data responsibly, they can: ✔ Design smarter wellness programs ✔ Identify patterns that lead to burnout ✔ Reduce sick days ✔ Improve overall team performance For individuals, it’s like having a micro-coach on your wrist. A gentle reminder to breathe. To stand up. To recover. To sleep better. And those small nudges? They compound. But let’s be clear: innovation without trust doesn’t work. If wearables enter the workplace, three things are non-negotiable: 1️⃣ Data must be aggregated and anonymized 2️⃣ Participation must be voluntary 3️⃣ Transparency must be total Technology should empower—not monitor. Used ethically, wearables can shift the conversation from “How many hours did you work?” to “How sustainably are you performing?” That’s a powerful change. So I’m curious: would you be open to using a company-provided wearable if it meant better health insights and performance support? Share your thoughts in the comments 👇 And follow me for more insights.

🔍 Understanding ML Time Complexities Time complexity measures how the runtime of a machine learning algorithm scales with the input size and parameters, helping us choose the right model for the data volume and application type. ⸻ 📈 Common Terms in the Table • n: Number of data points • p: Number of features • T: Number of trees (in ensemble methods) • l: Number of iterations (e.g., for K-Means) • k: Number of clusters or neighbors • m: Number of principal components • h: Number of hidden units in neural networks ⸻ 🧩 Interpretation & Practical Insights 1. Training vs. Inference • Some models (like KNN) are fast to train but slow to infer. • Others (like Random Forests, GBT) require longer training time but have quick inference. 2. Scalability • Linear/Logistic Regression scale well with data but poorly with high dimensions due to p³ cost. • PCA is sensitive to high feature counts. • Gradient Boosting and Random Forests scale better with big datasets but at the cost of training time. 3. Neural Networks • Complexity depends on layers, neurons, and batch sizes. • Proper tuning can mitigate high training costs. ⸻ 💡 When to Use What • 📊 Fast + Interpretable: Linear/Logistic Regression, Naive Bayes • 🌲 High Accuracy, Slower Train: Random Forest, GBT • 🧮 Unsupervised Dimensionality Reduction: PCA • 🔍 Instance-based / Lazy Learning: KNN (avoid with large n) • 🧠 Deep Learning: Neural Nets for complex, unstructured data (images, text) ⸻ 📌 Key Takeaways • Big-O analysis is essential for choosing the right model, especially with large-scale data. • Always consider feature count (p) and data size (n) together. • Use tools like profilers (PyTorch, TensorFlow) to assess real-world performance.

“History Repeats Itself” - About 40 years ago, Walmart CEO Sam Walton came to Hughes with a problem. His customers were leaving carts full of merchandise at the checkout lines. They were tired of waiting for long credit card transactions to complete, which could take minutes in the days of dial-up modems. Hughes solved the problem by commercializing two-way satellite communications to create an always-on network. Credit card transactions at Walmart went from minutes to seconds. Big box retailers, specialty retailers, convenience stores, restaurants, and anyone dependent on fast credit card transactions gravitated toward Hughes for a satisfying customer experience. Fortune identified this innovation as one of the top 20 historical decisions that shaped the modern business world. Today, 40 years later, history is repeating itself. As enterprise companies become increasingly dependent on digital technologies, new requirements that exceed today’s Internet capabilities are emerging. When a best-efforts Internet connection stands in between a company's ability to execute day-to-day functions and engage with customers and employees, simply hoping there is enough bandwidth to support all the mission-critical applications is not enough. Even if today’s performance is deemed acceptable, what about tomorrow? How much longer before rapidly evolving business requirements exceed the Internet’s static capabilities for bandwidth and availability? Imagine a world where retailers entice shoppers with live shopping video broadcasts, remote experts provide immersive AR/VR support, and #AI-enabled virtual assistants manage frontline operations. This is already happening in other parts of the world and for certain industries. This will be the new normal for Internet expectations and is within reach. Today’s enterprise networks, dependent on minimal contention to communicate, were not designed to meet the demanding requirements for guaranteed bandwidth access, ultra-high capacity, and ultra-low latency. A new approach is needed. Private 5G with inherent capabilities such as dynamic network slicing will satisfy these new needs while elevating an enterprise’s cybersecurity posture. As we did 40 years ago, Hughes is commercializing new technology, namely private 5G, to address new market requirements. Last year, Hughes integrated and implemented a secure, private 5G network for a Naval Air Station Whidbey Island. With Echostar’s ownership of wireless spectrum and DISH’s buildout of America’s 4th 5G network, Hughes is well-positioned to satisfy the emerging enterprise need for #private5G. #TimTang

#PrivateNetworks Are Now Operational at Scale 🏭📡🚜- Across enterprise environments—manufacturing plants, logistics hubs, mines, construction sites, and smart farms—private LTE and 5G networks are no longer in trial mode. They're delivering deterministic performance, safety, automation, and resilience in the real world. The latest TeckNexus Private Networks: Enterprise Verticals edition showcases award-winning deployments, C-level interviews, and ecosystem insights from leaders transforming operations across sectors. Executive Spotlights & Real-World Deployments ✈️ Adam Schipper, CTP from Ericsson, and Mario Schwarz from Lufthansa Cargo share how a private 5G network at #LAX is boosting warehouse efficiency through real-time tracking, data access, and operational visibility. 🚗 Ericsson highlights how private 5G is accelerating smart manufacturing at JLR (Jaguar Land Rover)'s Solihull facility. Jan Diekmann ⛏️ From the gold mines of Brazil, Renato Bueno (Nokia), Ricardo Pianta (Venko Networks), and Lucas Fernando (Salinas Gold Mineração Ltda) detail how private LTE is enabling safe, connected mining in ultra-remote zones—built in collaboration with Ávato 🏗️ Satoru Yamamoto at NISHIMATSU CONSTRUCTION CO.,LTD. along with NTT DATA reveals how ultra-remote heavy machinery is now controlled via private 5G + APN—transforming safety and operations in hazardous construction zones. 📺 Mika Skarp (Cumucore) and Morten Brandstrup (TV 2 Danmark ) showcase how private 5G is powering daily live broadcasting across Denmark, reducing latency and boosting production agility. 🏟️ Boingo Wireless explains how private #CBRS networks are transforming the fan experience and operational connectivity across Rhode Island’s top sports & entertainment venues. Robin Wilson 📶 Fánan Henriques of Vodafone Business provides strategic insights in Mobile Private Networks: A Technology at a Turning Point—outlining enterprise adoption patterns, monetization models, and what's next. 🌾 Bhaskara Rallabandi at Invences Inc., in collaboration with Trilogy Networks, discusses how private 5G + #digitaltwins are enabling next-gen precision farming in rural America. 📦 Ken Zhang, CEO of EdgeNectar Inc, walks through a real-world deployment enabling warehouse automation with private 5G, edge computing, and deterministic wireless connectivity. 🌐 Joe Barrett, President of the GSA (Global mobile Suppliers Association), provides a market view in Private Mobile Networks: Market Status, Technical Foundations & the Path Ahead—covering adoption trends, ecosystem maturity, and what’s next for private wireless. 📥 Access the full edition - Link in the comments 👇 #Private5G #PrivateLTE #CBRS #EdgeComputing #5G #PrivateNetworks #SmartManufacturing #SmartFarming #MiningTech #SmartVenues #WarehouseAutomation | #GSA #Ericsson #LufthansaCargo #EdgeNectar #Invences #TrilogyNetworks #Vodafone #Boingo #TV2Danmark #Cumucore #NTTDATA #SalinasGold #Nokia #VenkoNetworks #Ericsson #JLR

🏥 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