Digital twin the daily life patterns using multi-modal data

Program Overview

This program involves developing a system that collects and analyzes data from wearables, smart devices, and the surrounding environment to recommend actions for assisting older individuals in their daily routines. The project explores multi-modal sensory data consensus and deep learning models to extract patterns of daily life. Candidates are also expected to address ethical considerations related to the research.

Program Outline

Other:

This project aims to create a demonstration system to collect, store, and recommend actions utilizing a collection of wearables, smart devices, utility usages, and surrounding living environments. It is anticipated that at least areas of multi-modal sensory data consensus and new progressive deep learning models are needed in this project to extract the 'patterns of daily life' and in return assist olderly in their daily routines. The successful candidate is also expected to work with the ethical implications related to this research.