Ambient factors such as light, heat, and music can significantly impact physical comfort in employees. Additionally, affective states and cognitive load of workplace occupants can affect their quality of life, general happiness, and cognitive capacity. This research aims to design and develop a smart workplace capable of detecting, analyzing, and acting on the environmental ambient factors, thus optimizing both physical comfort and happiness in employees in the workplace. To achieve this goal, this project targets the development of tools and methods using artificial intelligence, machine learning, and deep learning for ubiquitous monitoring, analysis, and interpretation employee happiness, cognitive load, and physical comfort based on information obtained from a variety of different wearable and non-contact sensors. Moreover, algorithms and models will be developed for aggregation of individual preferred and perceived ambient conditions, and cognitive/affective states. Finally, the obtained information will be used to autonomously and intelligently tune ambient and organizational parameters for optimum employee happiness, comfort, and performance. With the recent COVID-19 pandemic and the necessity for organizations to adopt to new workplace models, particular attention will be paid to distributed and remote working conditions. Machine learning will be used to understand affective and cognitive factors such as happiness, stress, and cognitive load from remote audio and video calls in addition to wearable sensors where possible.

Industry Partner(s):Bank of Montreal (BMO)

Academic Institution:Queen's University

Academic Researcher: Etemad, Ali

Focus Areas: Business Analytics

Platforms: GPU