i am thrilled to share that we have been given the opportunity to share our work through two papers to be read at this year's International Conference on Health and Social Care Information Systems and Technologies (HCist) :-)
this year's conference will take place as a hybrid event in Porto, from the 8th till the 10th of November.
An investigation on the effects of different types of odours on stress level of high school students when studying is co-authored with Wu Xinyue. its abstract reads:
In view of the mental health issues among adolescents in Singapore, aromatherapy is proposed to mitigate their stress level when studying. An experiment was conducted both in the classroom and home setting, to test the effectiveness of the give odours, lavender, rosemary, ylang-ylang, lemon and bergamot on reducing stress level, using the no odour scenario as control. Objective data (SpO2, heart rate and stress score) is collected using the Huawei Band 6 smartwatch. Subjective data was self-reported by the participants through Google forms, rating their emotional health on a 10-point scale and elaborating in prose. It was found that the anti-anxiety effects of the stimulants (lemon, bergamot, rosemary) were much larger than that of the sedatives (lavender, ylang-ylang). In particular, lemon showed the best objective anxiolytic effect, while bergamot was the best in terms of self-perceived effect. Rosemary relieved stress through raising productivity, but some effects of overworking were observed. On the other hand, ylang-ylang showed inconsistent effects, while lavender was not suitable to relieve stress when studying.
Designing and prototyping of AI-based real-time mobile detectors for calisthenic push-up exercise is co-authored with Zhang Xiyuan and Shawn Han. its abstract reads:
Fitness exercises, including push-ups, are very beneficial to personal health. Many Artificial Intelligence (AI)-based fitness trainers are developed based on human pose estimation models or assisted by Internet of Things (IoT) devices. However, many of them require access to a graphing processing unit (GPU) for model training or IoT sensors to deploy, less accessible for individuals. In our work, we designed and prototyped real-time mobile push-up detectors using three distinctive approaches: (1) Push-up pose classification, (2) Angle-heuristic estimation and (3) Optical flow detection. We trained our deep-learning model with over 2000 images to achieve a high accuracy for real time deployment. Models are tested on our video dataset applied data augmentation techniques to simulate real-world environmental conditions to evaluate model performance based on accuracy metrics (precision, recall, F1 score) and processing frame rate (FPS). From the results, we concluded that the angle-heuristic estimation method has the best overall performance and we analysed the reasons for the relatively poorer performance of the push-up pose classification and optical flow detection methods. All methods developed are capable of working on mobile devices without the need of GPU or IoT sensors.
