Grant: Collaborative Research and Development Grants
Ryerson University
$97,850.00
A cloud-based Machine Learning Framework for Assessment of Stress/Engagement through Multimodal Sensors_x000d_ _x000d_
FY 2020-21
Summary
Increased stress and anxiety can create a variety of problems within the human body, and can be especially detrimental to the immune system. Targeting stress relief prior to surgical operations can increase the ability of patients to heal faster. Non-pharmacological stress detection such as distraction using video games, music, and smartphone based behavioural interventions can deliver positive results without associated risks. Augmented/ Virtual Reality has been found to have specific benefits when used in stressful clinical settings, including, improved emotional well-being, increased positive mood shifts and decreased negative emotions. Shaftesbury Inc., in partnership with hospitals and clinics has been developing a Positive Distraction Entertainment System (PDES). The purpose of the system is to automatically detect stress level in users based on physiological signals (e.g. heartrate) and provide an adaptive experience, where the experience (e.g. a movie/game) is dynamically adapted based on the assessed stress level in users. Such a system can go beyond the realm of healthcare, providing high entertainment value through dynamic experience. However, regardless of the domain, the core challenge in the PDES system is to develop a solid algorithm to assess the stress/engagement level of users when experiencing an entertainment product. Merely utilizing a single physiological signal such as heartrate will not be enough to accurately assess the stress/engagement level. The purpose of this project is to develop a multimodal cloud-based machine learning framework for automatic stress/engagement level assessment for users from multiple physiological (e.g. heartrate, EMG, respiration) and behavioural signals (e.g. gesture, facial expression), and provide intuitive and interpretable visualization and analytics tools that provides non-technological stakeholders the ability to easily fine-tune such algorithms. The proposed research will help to position Canada as a leader in adopting advanced technologies such as XR and machine learning in healthcare and entertainment, while the resultant technology transfer to Canadian industry will strengthen Canada's global competitiveness and create positive impacts to Canadian economy and society.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
537987-2018
Name nom
Khan, Naimul
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
2020
Competition year année de concours
2018
Award amount
97850
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
Installment
inst:2 - 2
Partie
1
Nb partie
2
Application title
A cloud-based Machine Learning Framework for Assessment of Stress/Engagement through Multimodal Sensors_x000d_
_x000d_
Keyword
NSERC
Application summary
Increased stress and anxiety can create a variety of problems within the human body, and can be especially detrimental to the immune system. Targeting stress relief prior to surgical operations can increase the ability of patients to heal faster. Non-pharmacological stress detection such as distraction using video games, music, and smartphone based behavioural interventions can deliver positive results without associated risks. Augmented/ Virtual Reality has been found to have specific benefits when used in stressful clinical settings, including, improved emotional well-being, increased positive mood shifts and decreased negative emotions. Shaftesbury Inc., in partnership with hospitals and clinics has been developing a Positive Distraction Entertainment System (PDES). The purpose of the system is to automatically detect stress level in users based on physiological signals (e.g. heartrate) and provide an adaptive experience, where the experience (e.g. a movie/game) is dynamically adapted based on the assessed stress level in users. Such a system can go beyond the realm of healthcare, providing high entertainment value through dynamic experience. However, regardless of the domain, the core challenge in the PDES system is to develop a solid algorithm to assess the stress/engagement level of users when experiencing an entertainment product. Merely utilizing a single physiological signal such as heartrate will not be enough to accurately assess the stress/engagement level. The purpose of this project is to develop a multimodal cloud-based machine learning framework for automatic stress/engagement level assessment for users from multiple physiological (e.g. heartrate, EMG, respiration) and behavioural signals (e.g. gesture, facial expression), and provide intuitive and interpretable visualization and analytics tools that provides non-technological stakeholders the ability to easily fine-tune such algorithms. The proposed research will help to position Canada as a leader in adopting advanced technologies such as XR and machine learning in healthcare and entertainment, while the resultant technology transfer to Canadian industry will strengthen Canada's global competitiveness and create positive impacts to Canadian economy and society.