Project Level: Winter

Project Duration: 

4 weeks, 20-36 hours per week.


Wrist pulse signals, commonly used in traditional oriental medicine, reflects important pathological changes in the body which may be utilized to characterize an individual’s health status. The traditional method of examination via palpation (where the practitioner uses fingertips to feel the radial pulse of the patient) is highly subjective and depends heavily on the practitioner’s experience. Modern technology allows digitized pulse signal to be taken in a more objective manner. In this project, we will explore computational tools for analyzing and interpreting these computerized time series pulse signals. We aim is to identify discriminatory features that could be useful for automated diagnosis of inflammation such as appendicitis, pancreatitis, and duodenitis.    

Expected Outcomes:

Gain hands-on experience analysing medical time series data.

Gain knowledge on state-of-the-art clustering methods.

Learn about data processing, feature extraction, data modelling, and model evaluation.

Develop the ability to implement statistical algorithms.

A written documentation of the work done.

An opportunity to present the results in conferences and/or in journal articles.  

Suitable for:

This project is open to students with a background in Statistics, Data Science, or closely related fields, who are in their 3rd year undergraduate program or above. The applicant is also expected to be familiar with programming. 

Further information:

Please contact Dr Sharon Leemaqz if you would like to know more or apply for the project.  

Project members

Dr Sharon Leemaqz

Senior Lecturer
School of Mathematics and Physics