Rate My Professor Prithwi Chakraborty

Chakraborty's research specializations include affective computing, emotion analysis, machine learning, and cognitive load projects. His key publications demonstrate influence in human-computer interaction, health applications, and computer vision. Notable works encompass 'The rise of office design in high-performance, open-plan environments' (Candido, Chakraborty, & Tjondronegoro, 2019), 'Continuous Learning without Forgetting for Person Re-Identification' (Sugianto, Tjondronegoro, Sorwar, Chakraborty, & Yuwono, 2019), 'myPainPal: Co-creation of a mHealth app for the management of chronic pain in young people' (Cooke, Richards, Tjondronegoro, Chakraborty, et al., 2021), 'Using viewer's facial expression and heart rate for sports video highlights detection' (Chakraborty, Zhang, Tjondronegoro, & Chandran, 2015), 'Automatic identification of sports video highlights using viewer interest features' (Chakraborty, Tjondronegoro, Zhang, & Chandran, 2016), 'Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings' (Chowdhury, Farseev, Chakraborty, Tjondronegoro, et al., 2017), 'A machine learning approach to identify fall risk for older adults' (Chakraborty & Sorwar, 2022), and 'Towards generic modelling of viewer interest using facial expression and heart rate features' (Chakraborty, Tjondronegoro, Zhang, & Chandran, 2018). These publications underscore his ongoing impact in the field.