Wearable smart watches are increasingly being used for the detection of different health conditions by using machine-learning models to make inferences. We are, in particular, interested in the UoA developed MoodAI platform, which is developed for real-time mood state classification, based on the data obtained from Fitbit smart watches. The current platform, while being able to perform mood state classification using deep learning models, has some limitations. First, it only deals with data from Fitbit devices. Second, the ML models are black-box in nature and hence not explainable.
Wearable devices from many different vendors have their APIs and hence accessing wearable data from a range of wearable smart watches remains challenging. In this project, we seek to develop an unified interface that will enable data capture and processing from many different smart watches. More importantly, we will develop explainable deep ML models, which not only detect the onset of depression but also identify the key reasons for such a diagnosis, on an individualised basis. This project will be jointly supervised by Dr. Fred Sundram, who is a clinical psychiatrist and is based at FMHS.
Undergraduate
1) We will reuse a app and website developed by Part 4 students in 2023. We have also an ethics application in the pipeline
2) You will mainly work on paticipant recruitment, data acquisition, machine learning methods
3) You will also develop methods for explainability
None
No lab has been assigned to this project