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

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities.

Study by: Kiemute Oyibo, Kang Wang, and Plinio Morita

Read the full article here: https://doi.org/10.2196/41942

📢 Calling all caregivers and seniors! 🌟 Participate in our study and help revolutionize the concept of self-sufficient living with intelligent home sensors! 🏠💡

🔬 We are creating an innovative platform that models and identifies individual daily activities, empowering caregivers to monitor their loved ones who live independently. 🧑‍🦳🏡

👥 Qualifications: Age 60 and above
⏰ Estimated duration: Approximately 3 hours
📝 Complete a brief pre-study questionnaire (10-12 minutes)
👣 Engage in everyday tasks in our lab while wearing wearable devices, following instructions (such as turning on lights, washing dishes, cleaning, reading books, and eating)

💰 Receive $15 per hour for your valuable involvement and contribute to the future of caregiving! 💙🤝

✅ Be part of this transformative research! Enroll today and make a lasting impact on the concept of independent living! 🌟📲

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! 🌟📲

Meet Dr Shahabeddin Abhari!

Dr Abhari is a Post Doctoral Fellow at UbiLab and is currently working on a project to develop standards for smart homes and communities interested in implementing active assisted living (AAL) technologies.

If you would like to learn more about Dr Abhari, please visit his Linkedin: https://www.linkedin.com/in/shahabeddin-abhari-ba01a688/

📢 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!

Our Team 💙

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

AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing

This article presents a cloud-based system for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment.

The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a dataset of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

Study by: Abedi, H., Ansariyan, A., Morita, P., Wong, A., Boger, J., and Shaker, G.
doi: 10.1109/JIOT.2023.3235268.