Radar Signal Processing and Machine Learning Methods for Human Activityand Fall Event Detection

Monitoring human activity and fall events is the cornerstone of medical applications. The rising costs of healthcare
and the aging of the population are factors that influence researches in the medical industry, mainly for the
development of assisted living and smart home. Several technologies have been proposed in the literature for
monitoring people and health care. Recently, radar technology for human activity monitoring, fall event and
presence detection is essential need of a patient, and this technology has attracted a lot of attention. With the
radar signal processing, it is also possible to send a rapid emergency alarm in the case of falls of people, and
medical emergency situations. The advancements in field of signal processing, machine learning and deep
learning have led to an evolution of new era for radar in field of medical applications, and the development of new
technological concepts which can alleviate these important health problems. In this context, our future work will
focus on the identification of gaps in existing systems, the analysis and optimization of reliable assistive devices,
for optimal detection of activity monitoring, movements, and falls of people.

Faculty Supervisor:

Wei-Ping Zhu

Student:

Kalpeshkumar Ranipa;Keyu Pan

Partner:

Moonshot Health

Discipline:

Other

Sector:

Information and cultural industries

University:

Concordia University