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BACKGROUND
METHOD
Theodora Chaspari, Ph.D., Professor of
Electrical Engineering, Texas A&M
Matthew Ahle,Graduate Student of Computational Data
Analytics, FIU
The Development of an mHealth Infrastructure for Child and Family Therapy
CONCLUSION
“Thismoodrecognizing
algorithmjustmightsaveyourrelationship.”– NBCNews
FEATURED
PATENTS
COLLABORATORS
• Couples carried smartphones and wore wearable sensors for one day• They provided hourly survey reports, including if and when they had conflict
1
4
6
9
10
Adela C. TimmonsUniversity of Southern California
FixedEffects B(SE)
Intercept(𝛾00k) 73.23(2.85)***Closeness(𝛾10k) -15.75(7.57)*
RandomEffects Estimate(SE)
Level1intercept(eijk) 197.79(19.67)
Level2intercept(u0jk) 114.15(48.96)
Level3 intercept(r00k) 10.91(14.96)
FixedEffects B(SE)
Intercept(𝛾00k) 73.35(3.89)***
Closeness(𝛾10k) -21.53(11.01)*
RandomEffects Estimate(SE)
Level1intercept(eijk) 638.07(63.19)
Level2Intercept(u0jk) 167.57(83.64)
Level3 intercept(r00k) 24.87(46.20)
Actual Closeness Predicted Closeness
CONTINUOUS HOURLY MOOD STATES
EXAMPLE OF IMPLEMENTED AUTOMATED MEASURES
Dating Aggression and Feelings of Closeness
RESULTS
Q Sensor Actiwave Nexus 5
PredictedActual
1. Timmons, A. C., Chaspari, T., Narayanan, S., & Margolin, G. (provisional patent filed March 2nd, 2018). A technology-facilitated support system for monitoring and understanding interpersonal relationships.
2. Timmons, A. C., Chaspari, T., Ahle, M., Narayanan, S., & Margolin, G. (provisional patent filed March 2nd, 2018). An expert-driven, technology-facilitated intervention system for improving interpersonal relationships.
EXAMPLES OF PREDICTED VS ACTUAL SCORES
TRY OUR PROTOTYPE ON YOUR MOBILE DEVICE
ALGORITHM DEVELOPMENT• Self-reports were used as the ground-truth criteria for
the machine learning algorithms• We started with easy-to-detect relationship states• We then recursively fed predicted scores into the new
models to improve the performance of later algorithms
WIREFRAME• Based on previous research and theory, we designed a
user interface for a mobile intervention system for use with families and couples
• After developing this measurement scheme, we built algorithms to automatically detect these constructs
THEORIZED COMPONENTS OF HEALTHY RELATIONSHIP FUNCTIONING
Connection Support Positivity
ACKNOWLEDGMENTSNSF Grant BCS-1627272 (Margolin, PI)NIH-NICHD R21HD072170 (Margolin, PI)SC CTSI (NIH/NCATS) 8UL1TR000130 (Margolin, PI)NSF GRFP DGE-0937362 (Timmons, PI)NSF GRFP DGE-0937362 (Han, PI)APA Dissertation Research Award (Timmons, PI)
Shrikanth Narayanan, Ph.D., Professor of
Electrical Engineering, USC
MAE = 1"∑ |yi − yi%| "&'( IMAE =
|(1) ∑ |yi.
+yi- |) +(1) ∑ |yi
+yi. |))/01 |)
/01
(1) ∑ |yi.
+yi- |))/01
×100
• Conflict, divorce, and interpersonal violence have far-reaching economic, psychological, and health costs for children and their families1,2
• Family fragmentation is estimated to cost US taxpayers $112 billion annually3
• We propose a framework for developing a mobile intervention system for improving relationship functioning in families and couples
• Our goal was to use these data to develop machine learning algorithms that could be used to automatically and passively detect theoretically-relevant relationship states via mobile technologies
DISCRETE HOURLY INTERPERSONAL EVENTS
Kappa = .97Sensitivity = .98Specificity = .99Accuracy = .99
Kappa = .90Sensitivity = .92Specificity = .99Accuracy = .96
Interacting Conflict
Person 1 Person 2
MAE = 14.54IMAE = 29.11%Correlation = .75
MAE = 11.40IMAE = 46.22%Correlation = .85
Gayla Margolin,Ph.D., Professor of Clinical Psychology,
USC
• Results provide initial proof-of-concept that it is possible to detect indices of relationship functioning in daily life with reasonable accuracy
• Next steps involve building and launching the fully functioning application system to detect important relationship processes in real-life settings as they naturalistically unfold
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