Lecturer/Researcher at the School of Computing and Intelligent Systems.-Research focused on improvement of community based healthcare using Machine Learning and Sensors-Working with clinicians, industry and community healthcare providers to develop practical solutions-Co-Investigator on SENDoc project focused on smart devices for healthy aging (https://sendoc.interreg-npa.eu/)-Co-Investigator on CERT-D project focused on technology to support dementia (https://tinyurl.com/certd2022)-Co-Investigator on REAMIT proect focused on reducing food wastage using IoT and AI (https://www.reamit.eu/)

Visiting Lecturer @ UCD School of Public Health, Physiotherapy and Sports Science

Team Lead: Personal Sensing Group-Working with clinical and industry stakeholders on various health based problems.-Building Machine Learning and Data Science for Health problems-Research in the areas of machine learning and human behavior analysis

Research in the area of data analytics and machine learning for rehabilitation engineering.Title: Techniques for the Analysis of Human Motion and Activities for the purpose of Physiotherapy and RehabilitationThis project relates to the development of different algorithms and techniques in order to analyse human movements. Information from human movement can then be utilized in order to aid automatic feedback for physiotherapy. For example, exercise routines may be prescribed as part of a patients rehabilitation. During the performance of the prescribed exercise, motion analysis can be performed in order to ensure correct technique and adherence levels. One of the key problems here is the method of acquiring human motion data from patients in a non lab setting. Most often these exercises will be carried out at home and appropriate sensors are needed in order to detect motion without large hardware setups and costly sensors. One of the key elements of this project is to develop methods to analyse human motion using non-invasive methods which require low numbers of sensors. The second element of this research is then how this motion capture data is analysed such that accurate and appropriate feedback is given to the patient