Thu Tran

Thu Tran, Kenny Tsu Wei Choo, Shaohui Foong et al.

Monitoring swimmer performance is crucial for improving training and enhancing athletic techniques. Traditional methods for tracking swimmers, such as above-water and underwater cameras, face limitations due to the need for multiple cameras and obstructions from water splashes. This paper presents a novel approach for tracking swimmers using a moving UAV. The proposed system employs a UAV equipped with a high-resolution camera to capture aerial footage of the swimmers. The footage is then processed using computer vision algorithms to extract the swimmers' positions and movements. This approach offers several advantages, including single camera use and comprehensive coverage. The system's accuracy is evaluated with both training and in competition videos. The results demonstrate the system's ability to accurately track swimmers' movements, limb angles, stroke duration and velocity with the maximum error of 0.3 seconds and 0.35~m/s for stroke duration and velocity, respectively.

Linh Vu, Thu Tran

Given the global prevalence of cardiovascular diseases, there is a pressing need for easily accessible early screening methods. Typically, this requires medical practitioners to investigate heart auscultations for irregular sounds, followed by echocardiography and electrocardiography tests. To democratize early diagnosis, we present a user-friendly solution for abnormal heart sound detection, utilizing mobile phones and a lightweight neural network optimized for on-device inference. Unlike previous approaches reliant on specialized stethoscopes, our method directly analyzes audio recordings, facilitated by a novel architecture known as IConNet. IConNet, an Interpretable Convolutional Neural Network, harnesses insights from audio signal processing, enhancing efficiency and providing transparency in neural pattern extraction from raw waveform signals. This is a significant step towards trustworthy AI in healthcare, aiding in remote health monitoring efforts.