Biomedical Engineering Device Development

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

Real-Time Heart Rate Monitoring Using Computer Vision & Signal Processing ❤️📊 I’ve been working on an exciting project that combines computer vision, signal processing, and real-time data analysis to estimate heart rate (BPM) from facial detection using a webcam. 🎥💡 How It Works: ✅ Face Detection: Using cvzone‘s FaceDetector, we accurately locate the user’s face in real-time. ✅ Color Magnification: A Gaussian Pyramid is applied to amplify subtle color changes caused by blood flow. ✅ Fourier Transform: We extract frequency components corresponding to pulse rate. ✅ Bandpass Filtering: Only relevant heart rate frequencies (1-2 Hz) are retained. ✅ Visualization: BPM values are plotted dynamically for real-time monitoring. Tech Stack: 🖥️ OpenCV | 🧠 cvzone | ⚡ NumPy | 🎛️ FFT | 📈 Signal Processing Key Learnings & Challenges: 🔹 Fine-tuning parameters like Gaussian levels & frequency range significantly impacts accuracy. 🔹 Efficient real-time processing is critical to avoid lag. 🔹 Signal noise handling is essential for reliable BPM estimation. 🚀 This technique has potential applications in health monitoring, fitness tracking, and remote diagnostics. Would love to hear your thoughts on its real-world applications! #MachineLearning #ComputerVision #HealthTech #SignalProcessing #OpenCV #Python #RealTimeAI #BPMDetection

This paper explores the transformative impact of wearables and AI on healthcare workflows and patient care, focusing on enhanced efficiency, personalization, and cost-effectiveness. 1️⃣ IoMT (Internet of Medical Things) market is rapidly growing, projected to increase from $50.3 billion in 2020 to $135.87 billion by 2025, highlighting a significant shift toward digital health adoption. 2️⃣ Wearables have diverse applications, monitoring both biological factors (e.g., saliva, sweat) and utility-based measurements (e.g., smart fabrics, implants) to enhance patient data collection. 3️⃣ Real-time monitoring through wearables and AI supports early disease detection and continuous tracking, facilitating better treatment adherence and fewer hospital visits. 4️⃣ Patient interest in remote monitoring is strong, with 79% willing to use mobile ECG tools, and 74% feeling safer with constant monitoring, demonstrating growing acceptance of self-managed care. 5️⃣ AI-assisted monitoring with wearable sensors achieves high accuracy, including 97% accuracy in detecting atrial fibrillation, outperforming traditional methods. 6️⃣ AI models like deep learning and neural networks enable predictive diagnostics and personalized treatments, demonstrating 80% accuracy for heart disease, 80% for blood infections, and 94% for cancer detection. 7️⃣ Integration challenges include data management, EHR integration, privacy, bias, and transparency, all of which must be addressed to foster trust among healthcare providers and patients. 8️⃣ Automation potential is significant, with AI transforming tasks like medical billing, coding, and lab workflows, reducing errors and freeing up resources for patient care. 9️⃣ Future healthcare will increasingly depend on AI and wearables, reshaping patient management, especially for aging populations, and enabling personalized, real-time care delivery. 🔟 AI and wearables promise a comprehensive transformation of healthcare, enhancing efficiency, personalizing treatments, and reducing costs while overcoming obstacles to data integration and physician-patient trust. ✍🏻 Perry LaBoone, PE, CPA, PMP, Oge Marques. Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights. 2024. DOI: 10.1016/j.jjimei.2024.100294

The Peterson Center on Healthcare just released a timely and thought-provoking report titled on the evolving landscape of remote monitoring. As remote physiologic (RPM) and therapeutic monitoring (RTM) gain traction, especially in Medicare and Medicaid, this report asks a critical question: are we paying for what truly works? Key Findings: 📈 Use is growing rapidly: Medicare beneficiaries using RPM jumped from 44,500 in 2019 to 451,000 in 2023. RTM is also rising fast. 💸 Spending is accelerating: RPM spend in Traditional Medicare surged to $194.5M in 2023, with 22% of episodes lasting over 9 months. 🩺 Effectiveness varies widely by condition: -RPM for hypertension shows strong short-term results (up to 6 months). -RTM for musculoskeletal conditions helps when used during focused PT episodes (2–4 months). -RPM for type 2 diabetes shows only modest, short-lived benefit — mostly in patients with very high HbA1c levels (we know this from the last PHTI study) ⏳ Current billing doesn’t match the evidence: Providers can bill indefinitely, even after the clinical benefit has faded (the do-more-make-more problem with FFS). 📊 Data gaps are a big problem: It’s often unclear what’s being monitored, for whom, and why. We have a massive opportunity to align coverage and reimbursement with actual clinical value — ensuring remote monitoring improves outcomes and spending efficiency. As adoption accelerates, it's going to be critical that we develop payment policies and the appropriate clinical models of care to ensure the right tools are reaching the right patients — and only for as long as they help. PDF of full report attached. #DigitalHealth #RemoteMonitoring #ValueBasedCare #healthcare #healthcareonlinkedin #ChronicDiseaseManagement Meg Barron Caroline Pearson

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

What started as a bridge to transplant is becoming a bridge to better understanding the human heart. LVADs now generate vast streams of physiologic data, flow, pressure, power, vibration and this information is increasingly being integrated with AI driven analytics. Instead of simply keeping patients alive, these systems are beginning to help clinicians see patterns before symptoms appear. AI models can now predict pump thrombosis, anticipate right heart failure, and optimize speed settings based on individual physiology. This means fewer alarms, fewer hospitalizations, and more personalized management. The real progress isn’t in replacing clinical judgment but in augmenting it, giving teams richer insights to make earlier, better decisions. As AI becomes part of the LVAD ecosystem, the goal is simple: precision in every beat. Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. Image ref: Mayo Clinic #AIinMedicine #Cardiology #HeartFailure #LVAD #DigitalHealth #HealthcareInnovation #MedTech #ClinicalAI #PredictiveAnalytics #DataDrivenMedicine #ArtificialIntelligence #CardiacSurgery #SmartDevices #HealthTech #MachineLearning #MedicalDevices #BiomedicalEngineering #FutureOfHealthcare #PatientCare #InnovationInMedicine #AIIntegration

Black Mother Turns Near-Death Childbirth Experience Into Life-Saving Tech Company Ariana McGee is the CEO of Navigate Maternity, a life saving Tech Company, but her journey did not begin in a boardroom. It began in a hospital bed, where she nearly lost her life while giving birth. During that terrifying moment, Ariana realized a hard truth many Black women already know the healthcare system often does not listen to their pain or take their concerns seriously. Instead of waiting for the system to change, Ariana decided to build a solution herself. Using her experience in medical sales, she founded Navigate Maternity, a tech company focused on protecting pregnant and postpartum women before complications turn deadly. Navigate Maternity created the first remote monitoring system designed specifically for maternity care. The device tracks important health signs like blood pressure and oxygen levels from a patient’s home. Doctors can see warning signs of serious conditions such as preeclampsia in real time, allowing them to act faster and save lives. Ariana also successfully navigated the FDA process to get this technology approved and into the hands of those who need it most. Her story is a powerful reminder that when our needs are ignored, we can create our own safety, protect our communities, and build a lasting legacy.

Imagine a future in which the same Wi-Fi signals that stream our podcasts and deliver our emails quietly safeguard the people we care about. In 2013, researchers demonstrated that ordinary Wi-Fi reflections could operate as a form of sonar, capturing human movement through walls without cameras or wearables; at the time, the concept seemed far-fetched. A decade later, Carnegie Mellon University advanced the field, showing in January 2023 that a standard router, paired with machine-learning models, can reconstruct full human poses in real time, moving the idea from curiosity to commercial roadmap. Attention has since shifted to LoRaWAN, the ultra-low-power network best known for industrial and agricultural telemetry. Recent studies report 93 percent accuracy in detecting falls through walls at distances of up to ten metres. Healthcare technology providers are already offering LoRaWAN-enabled systems that monitor bed presence, mobility, and falls, issuing alerts that help older or infirm individuals remain independent for longer, and easing the financial pressures of residential care. The lesson is clear: transformational breakthroughs often arise not from novel inventions, but from re-imagining the overlooked capabilities of technologies already woven into our environment. As AI continues to percolate through our business and personal lives these novel solutions will continue to emerge. Existing business capabilities will undoubtedly be leveraged in new and innovative ways too.

I would like to share 3 papers that my lab has published/posted this week. The first paper is authored by Ying Jie Quek in ACS Nano. The paper describes the nano-electro-extraction system for longitudinal extraction of RNA from living 2D and 3D cell culture systems. This technique is helpful for researchers working on precious patient samples as we can extract RNA at multiple timepoints while preserving the spatial information of the tissue microenvironment. The second paper is authored by Ang Li in Biomaterials Science, invited for the Emerging Investigator series. Globally, we are seeing more conflicts, and we questioned what can biomaterial scientists do to better protect soldier's health. This perspective provides insights on the potential of regenerative medicine for use in military battlefields. The last preprint is authored by Intan Rosalina Suhito and it is under review. The intricacies of the human immunity cannot be easily replicated by animal models. In this paper, we demonstrate that the addition of stromal cells play a huge role to maintain immune cell viability, diversity, memory and ability to recognise naive antigens using our bioengineered tonsil organoid model. I would like to thank my co-authors for their contributions: Arun Kumar Ravi Kalaiselvi, Giulia Adriani, João F. Mano, Laurent David, Donovan Eu, Hong Sheng Cheng, Andrew Tan, Kai Sen Tan, Justin Chu, Yvonne Su, Gavin Smith and Chee Wah Tan. Links to papers: https://lnkd.in/gVuPs28Q https://lnkd.in/gacAgmgi https://lnkd.in/gDEVWrZU

The headline that caught my eye this week was "AI Trial to Spot Heart Condition Before Symptoms." Here's my take: Artificial intelligence holds substantial promise to improve quality and reduce costs in healthcare. One example from Leeds involves an algorithm that scours medical records for early warning signs of atrial fibrillation (AF) before symptoms appear — potentially preventing thousands of strokes. The results suggest that by analyzing existing medical records for patterns that human physicians might miss, AI can flag high-risk patients for early intervention. The trial has already identified cases like a 74-year-old former Army captain who had no symptoms but can now manage his condition effectively. This is particularly significant given that AF contributes to around 20,000 strokes annually in the UK alone. As Professor Chris Gale notes, too often the first sign of undiagnosed AF is a stroke — an outcome this technology could help prevent. The broader implication here is about AI's role in healthcare: not replacing physicians but augmenting their ability to identify risks earlier and intervene before conditions become critical.