Activity Prediction from Sensor Data using Convolutional Neural Networks and an Efficient Compression Method

Woojeong Jin, Dongjin Choi, Youngjin Kim, U Kang

http://doi.org/10.5626/JOK.2018.45.6.564

The identification of the number of occupants and their activities using the IoT system in a building is an important task to improve the power efficiency and reduce the cost of using smart cooling/heating systems. In the actual building management system, it is possible to use equipment such as a camera to understand the current situation in the room, and to directly determine the number of occupants and their types of behavior. However, identifying the number of people and behavior types in this way is inefficient and requires a large amount of storage space for data. In this study, indoor sensor data were collected using an infrared Grid-Eye sensor and noise sensor. Based on this data, we also propose a deep learning model that captures the number of participants and behavior patterns and a deep learning model that considers the temporal characteristics of data. The proposed model identifies the number of people with an accuracy of about 95.3% and human activities with an accuracy of 90.9%. We also propose a method to reduce the storage space while minimizing the loss of accuracy using truncated SVD.