TY - GEN
T1 - A human-centered smart home system with wearable-sensor behavior analysis
AU - Ji, Jianting
AU - Liu, Ting
AU - Shen, Chao
AU - Wu, Hongyu
AU - Liu, Wenyi
AU - Su, Man
AU - Chen, Siyun
AU - Jia, Zhanpei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extracted from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.
AB - Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extracted from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.
UR - https://www.scopus.com/pages/publications/85001086063
U2 - 10.1109/COASE.2016.7743529
DO - 10.1109/COASE.2016.7743529
M3 - 会议稿件
AN - SCOPUS:85001086063
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1112
EP - 1117
BT - 2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PB - IEEE Computer Society
T2 - 2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
Y2 - 21 August 2016 through 24 August 2016
ER -