News

The Oura Ring is a wearable technology that offers a sleek, screen-free design with strong comfort, long battery life, water resistance, and detailed sleep, recovery, stress, and cycle tracking. It provides rich biometric data, like heart-rate variability, temperature, and blood-oxygen saturation, and translates it into meaningful readiness and sleep insights.

Unlike bulkier smartwatches that people often remove for comfort or style, the Oura Ring is small, discreet, and jewellery-like, making it easy to wear consistently without clashing with outfits.

According to Dr. Plinio Morita, wearables like the Oura Ring can empower people to proactively manage their health by giving them accessible, personal data to guide better wellbeing decisions.

Read the full article here. 

During the 2021 B.C. heat wave, 98% of the 619 deaths happened at home.

UbiLab is using smart thermostats and AI to track indoor temperatures, predict which households are most at risk, and build real-time dashboards to support emergency response.

The goal is to protect vulnerable groups in a timely manner: older adults, low-income families, and those without air conditioning. 

Read the full article here. 

Our researchers are developing a machine-learning-powered warning system to detect dangerously high indoor temperatures in homes across North America.

Using smart thermostat data from 2015 to 2024, the system identifies when homes exceed 26°C for long periods, especially in dwellings without air conditioning.

The goal is to alert vulnerable residents and help municipalities plan interventions, such as distributing cooling resources and deploying emergency services.

You can read the full CTV News article here. 

The sheer volume of social media posts published on a daily basis means health experts hoping to set the record straight face a near impossible challenge – how do you know which claims will sputter out, which ones will gain momentum, and the best way to counteract false messaging?

These are some of the questions researchers at the University of Waterloo in Ontario hope to answer with a tool used to identify health misinformation on social media. Named U-MAS, short for UbiLab Misinformation Analysis System, the University of Waterloo research tool is able to track health misinformation patterns before they become potential catastrophes.

Read the full article here.

Self-serve machines have become an ubiquitous way to place restaurant orders, buy transit tickets or purchase groceries. But for many consumers with disabilities, they are self-serve in name only. These machines often do not have features to make them accessible for consumers with disabilities, or consumers cannot find these features.

Few guidelines exist about how to make self-serve machines accessible to consumers with disabilities. A recent study from the University of Waterloo seeks to better understand the problems consumers with disabilities face when using self-serve machines, and how those barriers can be fixed.

Read the full article here.

Smart thermostats can provide insights into sleep quality, allowing researchers to infer a person’s sleep patterns without invasive monitoring. The finding is one of several developments presented this week at Sleep 2024, the annual meeting of the Associated Professional Sleep Societies.

Researchers analyzed eight terabytes of data collected from Ecobee smart thermostats in more than 178,000 households. Homeowners voluntarily contributed this data for energy efficiency research. The study team — led by postdoctoral researcher Jasleen Kaur — leveraged the Ecobee motion sensors to accurately identify complex sleep patterns and disturbances.

Read the full article here.

A new study to be presented at the SLEEP 2024 annual meeting offers a framework for an objective, non-invasive and zero-effort sleep monitoring system utilizing smart thermostats equipped with motion sensors.

Results show that smart thermostats identified three distinct sleep quality clusters, with clear variations in sleep duration, disturbances and efficiency. Comparative analysis underscored the heterogeneity in sleep quality, highlighting the potential of smart devices and NextGen IoT data sources in identifying sleep patterns and contributing to sleep research without invasive monitoring.

Read the full press release here.

Read the abstract here.

For many, the word “blockchain” either conjures images of Bitcoin or is a cause for confusion. But the seemingly abstract technology typically reserved for cryptocurrency aficionados is on the verge of improving efficiency, transparency and trust in health-care settings.

“There’s tons of novel approaches [using blockchain], especially when it comes to managing data,” says Pedro Miranda, a PhD candidate and researcher with Ubiquitous Health Technology Lab at the University of Waterloo whose work has focused on harnessing the technology for use in health care and health research. However, Miranda cautions that there are still a number of limitations to the technology.

Read the full study here: https://healthydebate.ca/2023/04/topic/blockchain-future-of-medicine/

There's a new, non-invasive technology that monitors seniors in long-term care facilities without the need for cameras, fobs or other traditional wearable gadgets.

Researchers at the University of Waterloo in Ontario use a wall-hung, low-power radio system and artificial intelligence (AI) to take note of habits like how often residents go to the washroom, when they eat, or how long they usually watch TV. It can also alert care providers in the event of a fall.

Hajar Abedi is a PhD candidate in systems design engineering at the university and lead author of the study, which was published in the journal the Institute of Electrical and Electronics Engineers (IEEE) Internet of Things.

"We use artificial intelligence to actually make our lives easier because we can train them and they can do our job, and basically, our main purpose is to save lives using this AI technology," Abedi told CBC Kitchener-Waterloo.

Read the full article here! 

https://www.cbc.ca/news/canada/kitchener-waterloo/ai-monitoring-system-long-term-care-homes-university-waterloo-1.6800965

Using Apple Watch ECG Data for Heart Rate Variability Monitoring and Stress Prediction: A Pilot Study by  Velmovitsky, P.E., Alencar, P., Leatherdale, S.T., Cowan, D., and Morita, P.P. has been published on Forbes! 

This article pilots the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and applies 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.

Read here!

Article: https://www.forbes.com/sites/andrewwilliams/2022/12/30/study-suggests-apple-watch-is-ready-for-stress-tracking/?sh=589659171634

Study: https://www.frontiersin.org/articles/10.3389/fdgth.2022.1058826/full