Technology 4 Mental Health

News item:

Hugo Gonzalez's work on mood disorders and technology

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• Individual/group controlled immersive environments
• Sensors from above, actuators from above, and more
• Office/home adaptive treatment environment
• K. Passino, Seminar at Colorado School of Mines, 4/24/18, "Design of Psychophysical Ambience: Taking Off Your Virtual Reality Headset"

3D adaptive sound control:

• Experiment for sound volume/frequency control.
• Experiment in volume control for sound/music reproduction.
• 3D surround sound (being installed now)
   
  

2D adaptive light arrays progress ("smart lights"):

• Experiments in our Distributed Dynamical Systems Laboratory (see also iSTEM)
• Koroglu M.T., and Passino K.M., "Illumination Balancing Algorithm for Smart Lights," IEEE Trans. on Control Systems Technology, Vol. 22, No. 2, pp. 557-567, March 2014.
• Jose Velasquez and Kevin M. Passino, "Fuzzy Fault Tolerant Control for Smart Lights," Journal of Intelligent and Fuzzy Systems, Vol. 28, No. 6, pp. 2605-2620, 2015.
• Wilfredo Alfonso, Jose J. Velasquez, Kevin M. Passino, Eduardo F. Caicedo, "A Honeybee Social Foraging Algorithm for Feedback Control of Smart Lights," Engineering Applications of Artificial Intelligence, Vol. 48, pp. 13-31, 2016.

3D adaptive light arrays (in progress):

• Wireless lighting, zigbee-controlled LED bulbs
• Synchronized with sound (e.g., music)

2D/3D adaptive temperature control arrays progress (on a planar grid):

• Experiment in our Distributed Dynamical Systems Laboratory (and laboratory assignment in ECE 5557)
• Quijano N., Gil A.E., Passino K.M., “Experiments for Dynamic Resource Allocation, Scheduling, and Control,” IEEE Control Systems Magazine, Vol. 25, No. 1, pp. 63-79, Feb. 2005.
• Quijano N., Andrews, B.W., Passino K.M., "Foraging Theory for Multizone Temperature Control," IEEE Computational Intelligence Magazine, Vol 1, No. 4, pp. 18-27, Nov. 2006.
• Quijano N., Passino K.M., "The Ideal Free Distribution: Theory and Engineering Application," IEEE Trans. on Systems, Man, and Cybernetics, Vol. 37, No. 1, pp. 154-165, Feb. 2007.
• Quijano, Nicanor, Passsino, Kevin M., "Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application," Engineering Applications of Artificial Intelligence, Vol. 23, pp. 845-861, 2010

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Methods for individual/group stress reduction:

1. Sensors: HRV, EEG
2. Inputs: Music, light, VR, adaptive ambience
3. Strategy: (i) feedback control of light/sound at individual/room level; (ii) VR: Mindfulness, gratitude, guided imagery

Stress Reduction with Music Modulation

1. Focus on individuals.
2. Sensor: PPG, compute HRV (SDNN)
3. Input: Tempo-modulated music, via feedback of stress
4. Controller: Proportional-integral (PI)
5. Results: Feedback control reduces stress during a type of Stroop test, where music or no-music results in increases in stress. 

Identifying Child Isolation in a Preschool Classroom

1. Isolation seems to occur due to choices by a child (move away from group, draw inward) or due to exclusion by other children. Each may cause stress in the child.
2. Computer vision approaches to detect and identify cases where children are isolated by physical distances.
3. Results: Use of an isolation/social clustering score, some validation that it succeeded. 

Adaptive Ambience for the Classroom:

1. College of Engineering / Schoenbaum Famiy Center collaboration, focus on preschool classrooms, toxic stress due to childhood trauma
2. Funded by the Battelle Engineering, Technology, and Human Affairs endowment.
3. Sense child HRV (watches), algorithmic computations (controller), and modulation of ambient light and sound for stress reduction.  Also, sense child voice and location (see above).
4. In progress, led by Dr. Hugo Gonzalez Villasanti at SFC, Crane Research Ctr.

PTSD:

1. Understanding symptoms, prevalence, treatment methods, and neuroscience
2. Research on novel treatment methods based on coordinated sound/light modulation.
3. In collaboration with McKayla Tassy, neuroscience major

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Focus: Addiction:

• Modeling, analysis, and technological intervention design for social (binge) drinking, especially for college-aged students.  
• A systems and control theoretic approach to understand group drinking.

Individual Drinking (in progress):

• Estimation of BAC (level of intoxication) from phone data and a biosensor.
• Technology implementation

Publications on Social Binge Drinking:

1. Luis Felipe Giraldo, Kevin M. Passino, and John D. Clapp, "Modeling and Analysis of Group Dynamics in Alcohol-Consumption Environments," IEEE Transactions on Cybernetics, Vol. 47, No. 1, pp. 165-176, January 2017.
2. Luis Felipe Giraldo, Kevin M. Passino, John D. Clapp, and Danielle Ruderman, “Dynamics of Metabolism and Decision Making During Alcohol Consumption: Modeling and Analysis,” IEEE Transactions on Cybernetics, Vol. 47, No. 11, pp. 3955-3966, Nov. 2017.
3. Clapp, J. D., Madden, D. R., Gonzalez Villasanti, H. J., Giraldo, L. F., Passino, K. M., Reed, M. B., & Fernandez Puentes, I. (In press). A System Dynamics Model of Drinking Events: Multi-Level Ecological Approach. Systems Research and Behavioral Science.
4. Gonzalez Villasanti, H. J., Passino, K. M., Clapp, J. D., Madden, D. R., "A Control-Theoretic Assessment of Interventions During Drinking Events," To appear, IEEE Trans. on Cybernetics, 2018.

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