Title: Human activity recognition and Identifying the activity patterns on noninvasive sensor data with Deep Learning
Speaker: Simon U
The recent advancement and development of both embedded electronic devices and deep learning techniques have made real-time activity tracking and monitoring feasible with the help of wearable devices or cameras. The time-series data can be analyzed and used to track an individual’s health condition and daily activities routine. But there’s a growing trend toward using noninvasive and non-visual activity sensing to get information and figure out what a person is doing without bothering them. No one wants to be constantly watched and recorded by cameras.
The current approach for human activity recognition using ambient sensors, such as motion sensors and light sensors, is restrictive and has poorer performance compared to approaches using cameras and wearable sensors. In this talk, I will discuss the challenges encountered on the task as well as potential approaches I hope to investigate in order to overcome some of these issues during my MInf project. The aim is to build a model that takes into account prior knowledge of common activity patterns for the activity recognition task and uses the same model to forecast the individualized activity routine.