Smartphone-based Qualitative Analyses of Social Activities During Family Time

With the evolution of nuclear families and diverse career options, families as social groups are spending lesser time together than in the past decades. In this work, we study both quantitative as well as qualitative aspects of time spent with family members through a smartphone-based pervasive study on a sample of 12 families over 14 days. Further, we also examine the perception of 78 millennials on what they feel about, and expect from, the time they spend with their families, however long it may be. We aim to identify the key parameters that shape family life in this day and age, along with examining the participation of individuals of various roles within the family in activities such as conversations, workout sessions, eating together and other social interactions. Among all activities detected to be performed by families reporting high satisfaction with familial life, Eating Together and Using Smartphones Together emerged as the most prominent ones. We discover a greater disparity among the habits of family members, especially millennials, staying away from each other as compared to those staying together.

[1]  Peter March,et al.  Stability of binary exponential backoff , 1988, JACM.

[2]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[3]  V. Bengtson Beyond the Nuclear Family: The Increasing Importance of Multigenerational Bonds , 2001 .

[4]  P. Amato,et al.  The legacy of parents' marital discord: consequences for children's marital quality. , 2001, Journal of personality and social psychology.

[5]  Paul Lukowicz,et al.  Can smartphones detect stress-related changes in the behaviour of individuals? , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[6]  Tanzeem Choudhury,et al.  Passive and In-Situ assessment of mental and physical well-being using mobile sensors , 2011, UbiComp '11.

[7]  Paul Lukowicz,et al.  Bluetooth based collaborative crowd density estimation with mobile phones , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[8]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[9]  P. Lukowicz,et al.  Collaborative Crowd Density Estimation with Mobile Phones , 2011 .

[10]  E. Burgess,et al.  The family : from institution to companionship , 1954 .

[11]  Tadashi Okoshi,et al.  Investigating interruptibility at activity breakpoints using smartphone activity recognition API , 2016, UbiComp Adjunct.

[12]  E. Burgess The Family as a Unity of Interacting Personalities 1 , 1926 .

[13]  Gang Chen,et al.  E2C2: efficient and effective camera calibration in indoor environments , 2015, UbiComp/ISWC Adjunct.

[14]  John A. Stankovic,et al.  M2FED: Monitoring and Modeling Family Eating Dynamics: Poster Abstract , 2016, SenSys.

[15]  E. Burgess,et al.  The Family: From Institution to Companionship , 1946 .

[16]  Katarina Dadić The Big Disconnect : Protecting Childhood and Family Relationships in the Digital Age , 2014 .

[17]  Koichi Kise,et al.  Quantifying the mental state on the basis of physical and social activities , 2015, UbiComp/ISWC Adjunct.

[18]  Archan Misra,et al.  GruMon: fast and accurate group monitoring for heterogeneous urban spaces , 2014, SenSys.

[19]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[20]  Archan Misra,et al.  Need accurate user behaviour?: pay attention to groups! , 2015, UbiComp.

[21]  L. Bumpass,et al.  Changing patterns of remarriage. , 1990 .

[22]  Andrew T. Campbell,et al.  BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing , 2014, Mobile Networks and Applications.