Sensor-Agnostic Machine Learning Models for AAL and Domotics Sensors

In an innovative collaboration, Ubilab at Waterloo University and the eBioMed platform at BMBI have developed a sustainable technological system to support elderly individuals living alone in retirement homes. This project, set to be deployed across six retirement homes in Canada, involves the use of minimal sensors to monitor and analyze the daily activities and potential emergencies of the residents. The system integrates sound recognition IoT devices, including microphones with Raspberry Pi boards, along with smart home sensors such as door contacts, motion detectors, temperature, and air quality sensors. Over a 12-month period, these devices will collect continuous data to provide insights into the Activities of Daily Living (ADL) and detect situations like distress or infectious illnesses. Leveraging Artificial Intelligence (AI) techniques, the project aims to analyze this data to enhance the safety and well-being of elderly individuals. Participants in this initiative will also focus on the maintenance and evolution of the system, ensuring its effectiveness and adaptability. This project represents a significant step towards utilizing technology to improve elderly care, showcasing the potential of AI and IoT devices in creating a safer environment for the aging population.

Faculty Supervisor:

Plinio Pelegrini Morita;John McPhee

Student:

Partner:

Université de Technologie de Compiègne

Discipline:

Engineering

Sector:

Education

University:

University of Waterloo

Program: