TY - GEN
T1 - FamilyPal
T2 - 15th IEEE International Conference on Industrial Informatics, INDIN 2017
AU - Gu, Fei
AU - Niu, Jianwei
AU - He, Zhenxue
AU - Jin, Xin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/10
Y1 - 2017/11/10
N2 - Taking part in family activities plays an important role in establishing good relationships with family members. It can solve the loneliness of elders, which related not only to their physical health, but also to the well-being of the whole family. In the paper, we propose FamilyPal, an effective system for detecting family activities, which can help users establish good relationship with family members. Specifically, FamilyPal firstly uses smartphones built-in sensors, such as GPS, accelerometer, microphone, gyroscope, and Wi-Fi to obtain the motion and location of users, the surrounding voice, etc. Secondly, with the sensed data, we propose an effective method based on Gaussian Mixtures Models (GMM) to detect family activities, including occurrence of meal, cooking, TV viewing, conversations, in an unobtrusive manner. Thirdly, we select appropriate sensors for classification to improve smartphones battery life. FamilyPal has been implemented on the Android platform and evaluation of the system with 10 subjects over one week shows that FamilyPal can accurately classify family activities with the average precision of 71%, the average recall of 73% and the F-measure of 71.99%.
AB - Taking part in family activities plays an important role in establishing good relationships with family members. It can solve the loneliness of elders, which related not only to their physical health, but also to the well-being of the whole family. In the paper, we propose FamilyPal, an effective system for detecting family activities, which can help users establish good relationship with family members. Specifically, FamilyPal firstly uses smartphones built-in sensors, such as GPS, accelerometer, microphone, gyroscope, and Wi-Fi to obtain the motion and location of users, the surrounding voice, etc. Secondly, with the sensed data, we propose an effective method based on Gaussian Mixtures Models (GMM) to detect family activities, including occurrence of meal, cooking, TV viewing, conversations, in an unobtrusive manner. Thirdly, we select appropriate sensors for classification to improve smartphones battery life. FamilyPal has been implemented on the Android platform and evaluation of the system with 10 subjects over one week shows that FamilyPal can accurately classify family activities with the average precision of 71%, the average recall of 73% and the F-measure of 71.99%.
KW - Family Activities
KW - Gaussian Mixtures Models (GMM)
KW - Mobile Sensing
KW - Smartphones
UR - https://www.scopus.com/pages/publications/85041190899
U2 - 10.1109/INDIN.2017.8104763
DO - 10.1109/INDIN.2017.8104763
M3 - 会议稿件
AN - SCOPUS:85041190899
T3 - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
SP - 155
EP - 160
BT - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2017 through 26 July 2017
ER -