Sarah Anderson’s Post

Are you curious about the latest breakthroughs in Parkinson's Disease (PD) research? Join Clario and esteemed guest speakers for an illuminating discussion on the revolutionary role of wearable sensor technology in tracking disease progression. Here's What You'll Learn: 🔹 How digital endpoints of gait and balance, powered by machine learning, outshine traditional rating scales in detecting PD progression. 🔹 The transformative potential of capturing composite scores of bradykinesia through wearable sensors for unparalleled disease monitoring. 🔹 Future trajectories of wearable sensor integration in clinical trials, illuminating the path towards precise neurodegenerative disease monitoring. Don't miss this unparalleled opportunity to be at the forefront of Parkinson’s research! Secure your spot now: https://lnkd.in/gaWjm5-v #ParkinsonsResearch #WearableTech #DigitalEndpoints #Neuroscience #Webinar

One week left to register and attend the live webinar! Are you curious about the latest breakthroughs in Parkinson's Disease (PD) research? Join Clario and esteemed guest speakers for an illuminating discussion on the revolutionary role of wearable sensor technology in tracking disease progression. Here's What You'll Learn: 🔹 How digital endpoints of gait and balance, powered by machine learning, outshine traditional rating scales in detecting PD progression. 🔹 The transformative potential of capturing composite scores of bradykinesia through wearable sensors for unparalleled disease monitoring. 🔹 Future trajectories of wearable sensor integration in clinical trials, illuminating the path towards precise neurodegenerative disease monitoring. Don't miss this unparalleled opportunity to be at the forefront of Parkinson’s research! Secure your spot now: https://lnkd.in/gR68ZwgP #ParkinsonsResearch #WearableTech #DigitalEndpoints #Neuroscience #Webinar

TBioCAS is proud of highlight one of its featured papers for August "BrainFuseNet: Enhancing Wearable Seizure Detection Through EEG PPG-Accelerometer Sensor Fusion and Efficient Edge Deployment". 📝 Authored by: Thorir Mar Ingolfsson, Xiaying Wang, Upasana Chakraborty, Simone Benatti, Adriano Bernini, Pauline Ducouret, Philippe Ryvlin, Sándor Beniczky, Luca Benini, and Andrea Cossettini. Volume: 18, Issue: 4, August 2024 The paper introduces BrainFuseNet, a novel, lightweight neural network designed for wearable devices to detect seizures by fusing electroencephalography (EEG), photoplethysmography (PPG), and accelerometer (ACC) signals. The network employs a Sensitivity-Specificity Weighted Cross-Entropy (SSWCE) loss function to address imbalanced datasets, achieving high seizure detection rates on the CHB-MIT and PEDESITE datasets with low false positive rates. BrainFuseNet is tailored for low-power embedded platforms and has been evaluated on the GAP9 microcontroller, demonstrating high energy efficiency and low power consumption, making it suitable for long-term monitoring on energy-constrained edge devices. 🔗 Read more on IEEE Xplore: https://loom.ly/Bm-Nteo #TBioCAS #Biomedical #CircuitsandSystems #paperhighlight

Just remember that the collected data points need to be properly and clinically validated.

Measuring long-term heart stress dynamics with smartwatch data: Biomedical engineers have developed a method using data from wearable devices such as smartwatches to digitally mimic an entire week's worth of an individual's heartbeats. The new 'digital twins' computational framework captures personalized arterial forces over 700,000 heartbeats to better predict risks of heart disease and heart attack. The advance is an important step toward evaluating the risks of heart disease or heart attack over months to years. #ScienceDaily #Technology

Exciting news in the fight against #breastcancer-related Lymphedema! We've just completed a comprehensive #scopingreview, analyzing studies to uncover the latest in sensor technology for lymphedema monitoring post-mastectomy. From wearable wonders to advanced nonwearable tech, discover how these innovative devices are changing the game in #patientcare. Join the conversation, share your thoughts, and let's explore these technological marvels together! 💡 #BreastCancerAwareness #HealthTechRevolution https://lnkd.in/gSqRjs4B

Measuring long-term heart stress dynamics with smartwatch data: Biomedical engineers have developed a method using data from wearable devices such as smartwatches to digitally mimic an entire week's worth of an individual's heartbeats. The new 'digital twins' computational framework captures personalized arterial forces over 700,000 heartbeats to better predict risks of heart disease and heart attack. The advance is an important step toward evaluating the risks of heart disease or heart attack over months to years. @Poseidon-US #ScienceDaily #Technology

Finnish history and expertise in microelectronics, sensors, chips, embedded hardware and software has led to a rich ecosystem around medical devices and health wearables. Yes, ŌURA and Polar Electro Oy are not the only ones. This article presents five Finnish wearable startups in patient monitoring, cardiac arrhythmia, glucose monitoring, and mental health. #wearables #health #finland #medtech #devices #sensors

What an amazing review this is on Wearables in Clinical Practice for Depression. I was particularly taken but this data framework: ⌚ Raw measurement from wearable sensors ( data cleaned and processed ) ➡ Low level features obtained ( data mapped against contexts like location, time of day ) ➡ High level patterns of potential clinical relevance ( machine learning ) ➡ Clinical states related to depressive symptoms Cardiology could learn some lessons from this elegant framework...anyone interested in working on it together?

👉🏼 Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia 🤓 Rong-Xin Guo 👇🏻 https://lnkd.in/eU3KQs7d 🔍 Focus on data insights: - The rapid growth in computational power, sensor technology, and wearable devices has revolutionized cardiac arrhythmia care. - AI plays a crucial role in prevention, risk assessment, diagnosis, and treatment of arrhythmia. - AI can enhance accuracy in arrhythmia diagnosis by detecting electrode misplacement or erroneous swapping. 💡 Main outcomes and implications: - AI advancements have led to more precise algorithms for arrhythmia diagnosis and personalized risk assessment. - Remote monitoring has expanded with contactless monitoring technology and wearable devices. - Future directions include exploring rare or complex types of arrhythmia using AI. 📚 Field significance: - AI is transforming the landscape of cardiac arrhythmia care through data-driven insights and technological advancements. 🗄️: #cardiacarrhythmia #artificialintelligence #datainsights