UbiLab - Ubiquitous Health Technology Lab

🔬 Innovative Research: "Tweeting for Health: Real-Time Mining and AI-Based Analytics for Misinformation Data Ecosystem on Twitter" 🔬

Infodemics have become an important concern and it is no surprise that this topic has gained significant attention in recent times. At UbiLab, we understand how important this issue is and are developing innovative solutions to fight against infodemics. UbiLab's Approach to Managing Infodemics- We are excited to share our innovative research project, the UbiLab’s Misinformation Analysis System (U-MAS). This project designed and developed a big data pipeline and ecosystem to identify and analyze health-related falsehoods, misinformation, and disinformation disseminated via social media. By closely monitoring and analyzing trends through ethically tested means, we empower government organizations to intervene proactively at the early stages of an infodemic.

⭐Recent Publications and Recognitions for our Research Project in 2023⭐

📚 JMIR publication: "Tweeting for Health using Real-Time Mining and AI-Based Analytics: Design & Development of a Misinformation Data Ecosystem for Twitter" (IF: 7.08)
Plinio Morita, PhD MSc PEng, Irfhana Z., Jasleen Kaur, PhD, Matheus Lotto, Zahid Butt
Read full article: https://lnkd.in/gzcEKfrY

📚 JMIR publication: "Topic Modelling Analysis of Fluoride-Related Misinformation on Twitter: An Infodemiology Study" (IF: 7.08)
Matheus Lotto, Irfhana Z., Jasleen Kaur, PhD, Zahid Butt, Thiago Cruvinel, Plinio Morita, PhD MSc PEng
Read full article: https://lnkd.in/gDERfBsj

📚 Frontiers publication: “Ethical principles for infodemiology and infoveillance studies concerning infodemic management on social media" (IF: 6.461)
Matheus Lotto, Thoko Hanjahanja-Phiri, PhD, Halyna Padalko, Arlene Oetomo, Zahid Butt, Jennifer Boger, Jason Millar, Thiago Cruvinel, Plinio Morita, PhD MSc PEng
Read full article: https://lnkd.in/gi3gFdcx

📚 Presented at World Congress on Public Health (WCPH'23): "Preventing Public Health Crises: An Expert System using Big Data and AI in Combating the Spread of Health Misinformation"
Irfhana Z., Jasleen Kaur, PhD, Matheus Lotto, Zahid Butt, Plinio Morita, PhD MSc PEng
Read full article: https://lnkd.in/gfMxDCxj

📚 Presented at World Congress on Public Health (WCPH'23): "Fluoride-Related Misinformation Analysis on Twitter: An Infodemiology Study"
Matheus Lotto, Irfhana Z., Jasleen Kaur, PhD, Zahid Butt, Thiago Cruvinel, Plinio Morita, PhD MSc PEng
Read full article: https://lnkd.in/g3sSntCW

📚 Presented at e-health workshop (ehpwas'23): "Design & Development of Misinformation Analysis System for Government Prevention of Public Health Crisis"
Irfhana Z., Jasleen Kaur, PhD, Matheus Lotto, Zahid Butt, Plinio Morita, PhD MSc PEng

Tweeting for Health Using Real-time Mining and Artificial Intelligence–Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter

Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics.

Study by: Morita, P.P., Zakir Hussain, I., Kaur, J., Lotto, M., Butt, Z.A.

Read the full study here: https://doi.org/10.2196/44356

Meet Dmytro Chumachenko!

Dmytro is currently engaged in research related to population dynamics simulation, including the dynamics of the epidemic processes of infectious diseases. His research is also devoted to various aspects of data-driven medicine and public health informatics.

If you would like to learn more about Dmytro, please visit his Linkedin: https://www.linkedin.com/in/dichumachenko

📢 Attention caregivers and seniors! 🌟 Join our study and help revolutionize independent living with smart home sensors! 🏠💡

🔬 We're developing a groundbreaking platform that models and recognizes personal daily living activities, enabling caregivers to monitor their family members living independently. 🧑‍🦳🏡

👥 Requirements: 60 years old or over
⏰ Total duration: Approx. 3 hours
📝 Complete a pre-study questionnaire (10-12 minutes)
👣 Perform daily activities in our lab while wearing wearable devices, with guidance (e.g., turning on lights, washing dishes, cleaning, reading books, eating)

💰 Earn $15/hr for your valuable participation and contribute to the future of caregiving! 💙🤝

✅ Be a part of this transformative research! Sign up today and make a lasting impact on independent living! 🌟📲

📢 Calling all seniors! 🙋‍♀️🙋‍♂️ Be a part of our groundbreaking study and help shape the future of caregiving! 🏡💙

🔬 We're developing a platform that models and recognizes personal daily living activities, aiming to empower caregivers and care providers to monitor family members living independently using smart home sensor data. 🌟✨

👥 Requirements: 60 years old or over
⏰ Total duration: Approx. 3 hours
📝 Complete a pre-study questionnaire (10-12 minutes)
🏠 Perform daily activities in our lab while wearing wearable devices, with guidance (e.g., turning on lights, washing dishes, cleaning, reading books, eating)

💰 Reward: Earn $15/hr for study participation!

✅ Don't miss this opportunity to be a pioneer in research and make a difference!

Using Apple Watch ECG Data for Heart Rate Variability Monitoring and Stress Prediction: A Pilot Study

**PUBLISHED ON FORBES**

This article pilots the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants.Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.

Study by: Velmovitsky, P.E., Alencar, P., Leatherdale, S.T., Cowan, D., and Morita, P.P.

Read here!
DOI: 10.3389/fdgth.2022.1058826