Grant: Collaborative Research and Development Grants
Ryerson University
$47,500.00
Signal processing based psychophysiological data analysis and ailment prediction for sport training
FY 2018-19
Summary
A variety of mobile applications have been introduced to market for health and fitness management. These applications allow users to record their health related data. However, they do not currently possess advanced technology and analytical tools to elaborate the hidden relationship in the data, let alone to forecast the possible illness, injury, and burnout risk in athletes (collectively known as ""ailment"" risks).*This research is a collaborative effort between Ryerson University and RightBlue Labs to utilize advanced statistical analysis, signal processing, and machine learning to: (i) investigate relationships between psychophysiological data, including measured psychological, activity, and physiological data, and various known health/performance indicators; and (ii) develop a set of analytical algorithms to predict/monitor health status and assess illness, injury, and burnout risks in athletes (ailment risks), with the potential to extend these findings to the general population.*Theory and application development from this research will build a strong technology foundation for mobile health monitoring and consultation systems with a strong commercial application focus, and provide a solid framework, useful guidelines, and tested algorithms for future research. This collaborated project will help Ryerson University to train highly qualified personnel and develop advanced expertise in health management research and big data analytics, and help RightBlue Labs and related Canadian industries to build and sustain market advantages.*
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
492939-2015
Name nom
Zhang, XiaoPing
Department département
Electrical, Computer and Biomedical Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2015
Award amount
47500
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2514
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Digital signal processing
Research subject fr
Traitement des signaux numériques
Partie
3
Nb partie
3
Application title
Signal processing based psychophysiological data analysis and ailment prediction for sport training
Keyword
NSERC
Application summary
A variety of mobile applications have been introduced to market for health and fitness management. These applications allow users to record their health related data. However, they do not currently possess advanced technology and analytical tools to elaborate the hidden relationship in the data, let alone to forecast the possible illness, injury, and burnout risk in athletes (collectively known as ""ailment"" risks).*This research is a collaborative effort between Ryerson University and RightBlue Labs to utilize advanced statistical analysis, signal processing, and machine learning to: (i) investigate relationships between psychophysiological data, including measured psychological, activity, and physiological data, and various known health/performance indicators; and (ii) develop a set of analytical algorithms to predict/monitor health status and assess illness, injury, and burnout risks in athletes (ailment risks), with the potential to extend these findings to the general population.*Theory and application development from this research will build a strong technology foundation for mobile health monitoring and consultation systems with a strong commercial application focus, and provide a solid framework, useful guidelines, and tested algorithms for future research. This collaborated project will help Ryerson University to train highly qualified personnel and develop advanced expertise in health management research and big data analytics, and help RightBlue Labs and related Canadian industries to build and sustain market advantages.*
