Grant: Alliance Grants
Trinity Western University
$20,000.00
Development of a Novel Integrative Recovery Index Using Machine Learning
EF 2022-23
Résumé
Success in athletics usually requires well-designed training programs that balance training intensity/volume and adequate rest/recovery to allow for progressive physical and psychological adaptation. The challenge with developing training programs is to understand and assess the point at which training loads require attenuation. This reduces the risk of entering into a persistent and unwelcome fatigued state referred to as 'functional overreaching'. A comprehensive index that complements training programs to maximize training loads without compromising recovery currently does not exist. However, contemporary wearable smartwatch technology shows promise to develop such a parameter. Unlike other smartwatches currently available, the Phoenix smartwatch developed by our industrial partner Health Gauge is capable of capturing blood pressure metrics and integrating psychological qualitative response options related to mood states. The goal of this project is to work with Health Gauge to combine the unique capabilities of the Phoenix smartwatch with machine learning (ML) to develop a novel, optimized, training recovery index'. Phase 1 of the project will involve calibrating the Phoenix watch to lab-based biometric measures including heart rate, blood pressure, heart rate variability, temperature and blood oxygen saturation under resting, dynamic and prolonged (ambulatory) conditions in a sample of university athletes. Phase 2 will involve obtaining physiological and psychological variables from trained cyclists using the Phoenix to develop the recovery index with subsequent use of ML to build an embedding framework into the watch that will detect states and combine state recognition with training inputs using an extended short-term memory model. Once the model is fully developed, its ability to predict a 'functionally overreached state' can be validated in other populations. Health Gauge will incorporate this index into their application, making it accessible to Canadians. In addition to monitoring the readiness of athletes, we also see the potential for this recovery index to guide return-to-work policies and recommendations for emergency medical personnel, healthcare workers, law enforcement, and military personnel.
Programme
Alliance Grants
Bénéficiaire
Emplacement
British Columbia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
578515-2022
Name nom
Cote, Anita AT
Department département
Human Kinetics, School of
Organization id
6
Institution établissement
Trinity Western University
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
20000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
5502
Research subject group en
Animal biology
Research subject group fr
Biologie animale
Research subject en
Kinesiology
Research subject fr
Kinésiologie
Installment
inst:1
Partie
1
Nb partie
1
Application title
Development of a Novel Integrative Recovery Index Using Machine Learning
Application summary
Success in athletics usually requires well-designed training programs that balance training intensity/volume and adequate rest/recovery to allow for progressive physical and psychological adaptation. The challenge with developing training programs is to understand and assess the point at which training loads require attenuation. This reduces the risk of entering into a persistent and unwelcome fatigued state referred to as 'functional overreaching'. A comprehensive index that complements training programs to maximize training loads without compromising recovery currently does not exist. However, contemporary wearable smartwatch technology shows promise to develop such a parameter. Unlike other smartwatches currently available, the Phoenix smartwatch developed by our industrial partner Health Gauge is capable of capturing blood pressure metrics and integrating psychological qualitative response options related to mood states. The goal of this project is to work with Health Gauge to combine the unique capabilities of the Phoenix smartwatch with machine learning (ML) to develop a novel, optimized, training recovery index'. Phase 1 of the project will involve calibrating the Phoenix watch to lab-based biometric measures including heart rate, blood pressure, heart rate variability, temperature and blood oxygen saturation under resting, dynamic and prolonged (ambulatory) conditions in a sample of university athletes. Phase 2 will involve obtaining physiological and psychological variables from trained cyclists using the Phoenix to develop the recovery index with subsequent use of ML to build an embedding framework into the watch that will detect states and combine state recognition with training inputs using an extended short-term memory model. Once the model is fully developed, its ability to predict a 'functionally overreached state' can be validated in other populations. Health Gauge will incorporate this index into their application, making it accessible to Canadians. In addition to monitoring the readiness of athletes, we also see the potential for this recovery index to guide return-to-work policies and recommendations for emergency medical personnel, healthcare workers, law enforcement, and military personnel.