David Ward

Paula Lago

Assistant Professor, Electrical and Computer Engineering

Research areas: pervasive computing; human activity recognition; wearable sensors; machine learning for sensor data; data stream mining

Contact information

Email:

paula.lago@concordia.ca

ORCID:

0000-0001-5290-6486

Biography

Current Research

Dr. Lago is an assistant professor in the Department of Electrical and Computer Engineering at Concordia University in Canada. Her research is focused on Pervasive Computing for Health and Healthy Aging. She is a member of the PERFORM Center and engAGE, where she studies ways of understanding and measuring health status and outcomes in ecological and cost-effectives ways by using wearables, sensors at home and machine learning.

Education

Paula Lago has a Doctorate of Engineering from Universidad de Los Andes, Colombia. She received her Bachelor’s and Master Degree in Software Engineering from the same university.

During her PhD. Dr. Lago studied activity recognition and routine learning at homes from non-invasive sensors embedded in objects like doors, light switches, or electric appliances, at home. The routines are described by their context to account for the fact that people usually change their routines based on context changes like day of the week, weather, presence of visitors, etc. As an invited researcher in the Informatics Laboratory of Grenoble, Paula had the opportunity to live in Amiqual4Home Smart Home to evaluate these ideas.

Dr. Lago was also a postdoctoral researcher at Kyushu Institute of Technology from 2018 to 2020 under the supervision of Prof. Sozo Inoue. There, she worked on a project focused on how to use wearable-sensor-based activity recognition to optimize nurses work at nursing homes, specifically by reducing the time spent in documentation tasks. Her research looked at ways to better use data collected in laboratory settings for activity recognition models used in real-life.

Research activities

Sensors and Data Streams

Using sensors embedded in objects of smart environments or sensors embedded in wearable devices to understand people's needs or to enable personal awareness and reflection of behavior.

Pattern mining and machine learning for sensor data

Making sense of sensor data by recognizing activities, learning frequent patterns and understanding routines

Healthcare applications

Sensing and analysis brought together to monitor and predict health outcomes

Publications

Paula Lago, Moe Matsuki, Kohei Adachi, and Sozo Inoue. 2021. Using additional training sensors to improve single-sensor complex activity recognition. 2021 International Symposium on Wearable Computers. Association for Computing Machinery, New York, NY, USA, 18–22.
Inoue, S., Lago, P., Hossain, T., Mairittha, T., & Mairittha, N. (2019). Integrating Activity Recognition and Nursing Care Records: The System, Deployment, and a Verification Study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 1-24.
Paula Lago, Claudia Roncancio, Claudia Jiménez-Guarín. Learning and managing context enriched behavior patterns in smart homes. Future Generation Computer Systems, Volume 91, 2019, Pages 191-205.
The ContextAct@A4H Real-Life Dataset of Daily-Living Activities

Teaching

Winter 2022

COEN 6311 - Software Engineering

Summer 2022