SCIS Research Seminars by TRAN Ngoc Doan Thu, TAN Yi Zhen and TRUONG Quang Hai

Talk #1: Analyzing Swimming Performance Using Drone Captured Aerial Videos
by TRAN Ngoc Doan Thu, PhD Candidate

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.

Talk #2: How is Our Mobility Affected as We Age? Findings from a Users Field Study of Older Adults Conducted in an Urban Asian City
by TAN Yi Zhen, PhD Candidate

In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high value to any researcher understanding and attempting to transform the lifestyle of older adults.

Talk #3: Applicability and Challenges of Indoor Localization Using One-Sided Round Trip Time Measurements
by TRUONG Quang Hai, PhD Candidate

Radio Frequency fingerprinting, based on Wi-Fi or cellular signals, has been a popular approach for localization. However, real-world application adoptions have faced challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ∼1-2 meters accuracy in both 2D and 3D environments. This method relies on the Wi-Fi standard 802.11mc implemented in APs (two-sided RTT). However, one obstacle is that the number of APs satisfying this 802.11mc requirement is limited because the frequency of an AP upgrade to a newer version is not as frequent as other electrical equipment. The publication of Google’s Android 12, supporting one-sided RTT, enables RTT applicability in almost all AP models. This article synthesizes multiple experiments to evaluate the feasibility of one-sided RTT in indoor localization and describes in detail the effects of various factors such as different AP models, phone models, and burst sizes on the performance of localization accuracy. Despite existing challenges of applying one-sided RTT, this approach is lightweight, scalable, and could easily be utilized by wearable devices to provide reasonably accurate indoor localization.

TRAN Ngoc Doan Thu is a PhD candidate at the School of Computing and Information Systems, Singapore Management University, supervised by Professor Rajesh Krishna Balan. Her research interests focus on video-based analysis in sport.

TAN Yi Zhen is a PhD candidate at the School of Computing and Information Systems, Singapore Management University. Under the supervision of Professor Rajesh Krishna Balan, her research focuses on health sensing. She is currently working on using indoor localisation for the sensing of loneliness, investigating mobility of older adults, as well as human activity recognition with smartphone sensors. Prior to joining the PhD programme, she graduated from SMU with a bachelor’s degree in business management (Marketing and Analytics).

TRUONG Quang Hai is a PhD candidate at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). Under the supervision of Professor Rajesh Krishna Balan, his research primarily investigates indoor localization techniques through the integration of multiple sensor sources. This approach aims to compensate for the inherent limitations associated with individual sensors. His work predominantly addresses challenges in densely populated environments and areas where little to no prior knowledge is available.