TY - GEN
T1 - FamilyLog
T2 - 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017
AU - Bi, Chongguang
AU - Xing, Guoliang
AU - Hao, Tian
AU - Huh, Jina
AU - Peng, Wei
AU - Ma, Mengyan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.
AB - Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.
UR - http://www.scopus.com/inward/record.url?scp=85019589028&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019589028&partnerID=8YFLogxK
U2 - 10.1109/PERCOM.2017.7917847
DO - 10.1109/PERCOM.2017.7917847
M3 - Conference contribution
AN - SCOPUS:85019589028
T3 - 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017
SP - 21
EP - 30
BT - 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017
Y2 - 13 March 2017 through 17 March 2017
ER -