Lighting Systems that Think

Transcription

1 Lighting Systems that Think How embedded intelligence will drive research on Human Centric Lighting Robert Karlicek, Jr., Ph. D. Professor, Electrical, Computer and Systems Engineering Director, Smart Lighting Engineering Research Center Rensselaer Polytechnic Institute

2 Outline Smart Lighting for Human Centric Lighting Systems Approach to HCL Research Biometrics and Lighting Control Research tool and practical applications space Color Control in Human Centric Lighting Dynamic, interactive systems and research

3 The FIXTURES are WATCHING Ambient Light and PIR Motion Detection (relatively primitive technology) February 2014 Ambient light sensors (daylighting, energy savings) Passive IR (motion detection, occupancy estimation) Wireless communications between lights (coordination)

4 Smart Lighting: Embedded Intelligence!! Powerful computers are inexpensive Embedded sensors will let lighting systems see Data Collection will become a primary role of lighting Drivers: Global interconnectedness Advanced data processing platforms Powerful, low cost distributed computing ARM Cortex M0+ Freescale Kinetis KL x 2.0 mm < 30 New entrants in the lighting business!!!

5 Lighting Control Learn from Robotics Embedded Expert Driver Self Driving Cars Complex Sensor Networks Computationally Intense Improved transportation Less human stress Safer Self Commissioning Lighting Complex Sensor Networks Computationally Intense Improved Illumination Maximized light quality Minimized energy consumption Embedded Lighting Designer

6 Light systems that see The Plenoptic Function Sense the plenoptic light field (How does light flow) Sense the use conditions (How is the space used) Conserve privacy (as much as possible)

7 Light transport analysis Basics (Wang and Boyer, Smart Lighting ERC) Sparse, low cost sensors Estimate entire light field Fast mesh network (no latency) Adapts to Daylighting and Occupancy Lighting Systems that See Self Commissioning Machine Learning Always Right Lighting

8 Light transport analysis sensing Add one person to above scene z integral confidence map 3D confidence map Q. Wang, X. Zhang, K. L. Boyer, Occupancy distribution estimation for smart light delivery with perturbation modulated light sensing, Journal of Solid State Lighting :17, ISSN , doi: /s

9 Digitized lighting additional sense capability Color selective time of flight Illumination based RADAR Low Resolution location of everything Useful for Task Estimation (Li and Radke, Smart Lighting ERC) Coarse, real time 3D mapping helps the lighting system see

10 A time of flight tracking example Fixtures Robot Raconteur* Computation nodes Sensors * robotraconteur.com

11 Combine light transport and TOF Light Transport Feature Vector LEARNING Dynamic Lighting Control Intensity Variable SPD TOF Depth Feature Vector Biometrics Implicit Model Sensors integrated with neural networks that learn Ergonomic Lighting Quality and Energy Savings (corrected for human factors considerations)

12 Lighting and Human Performance Light drives both the visual system AND biological/behavioral effects VISUAL EFFECTS VISUAL REFLEXES BIOLOGICAL/BEHAVIORAL Acute Effects Longer Term Effects Melatonin Secretion Circadian Phase Shift Body Temperature Circadian Entrainment Cortisol Secretion Sleep Physiology Heart rate Light Therapy (e.g., SAD) Alertness Cognitive Performance Psychomotor Performance Brain Blood flow EEG Responses Clock Gene Expression

13 Circadian Rhythm as a Control System The Vision Personalized real time circadian estimation Optimized feedback lighting control Targeted application domain Integration with performance and health Disturbances Desired Rhythm Control Algorithm Circadian Estimation (Signal Processing, Sensor Fusion)

14 Increasing commercial interests State of the art Open loop entrainment Average human phase Response Curve (PRC) Questionnaire based circadian rhythm estimation Philips Re timer withings.com philips.com Withings CPAP circadian.com mybasis philips actigraph withing Smart Lighting ERC

15 Light based biometrics integration Advanced Sensor Data Analysis New Analytical Circadian Model Faster, More Accurate Circadian Phase Estimator Wearable Sensors for Light Based Circadian Optimization? Analytical Model for Circadian Phase Shifting with Light

16 Circadian Rhythm Estimation (Drosophila) Red pulse train Single blue pulse No phase shift π phase shift

17 Simplified optimal & adaptive control Same optimal cost for phase reduced model Adaptive Circadian Control Model works well for Drosophila, work underway to validate for people (active collaboration with Thomas Jefferson University and the University of New Mexico) lighting Circadian System Controller PRC model biometric ANF

18 Better Light Dose Measurements Lightfeed an undergraduate business startup (advised by the Smart Lighting ERC) Google Glass based unit shown at CES 2014 Spectrally resolved light dose measurements 10 nm buckets Wireless communications Long battery life being worn sensing Current low cost Prototype Better research tool, allows crowd sourcing of light dose information for large data analytics

19 Human Centric Lighting Illumination Video Fusion Simulated widow Smart Lighting ERC St. Peter s Albany Collaboration Light boxes on side match light expected based on display (sunny, cloudy, sunrise, sunset) Installed on ICU rooms without windows From The Sky Factory Dramatic effect on patient outcomes (to be published soon) Better than windows (change locations daily?) Installed in 19 ICU s Operating for 18 months Dramatic impact on patient care

20 New Lighting Dynamics Creative use of metamers at fixed u v based on sensor network input Context Sensitive Adaptive Color and Intensity Control Responds to human activity Stimulates alertness Therapeutic applications(?) Improved cognition (?) Other value added functions(?)

21 New Human Centric Applications Effect of dynamic spectrum on productivity Interactive illumination for improved health care outcomes Impact of dynamic color and intensity on education Other uses??? $$$$$$ The Smart Lighting ERC is currently seeking research partners for evaluating Smart Lighting Systems

22 Summary Sensor systems and integrated controls will revolutionize illumination Embedded systems (lighting designer inside) have the potential to autonomously optimize lighting for human consumption) Much more research is needed, we have only scratched the surface of what might be possible

23 Acknowledgements Funding from the NSF Colleagues at Rensselaer (lead) Boston University University of New Mexico Thomas Jefferson University The Smart Lighting ERC Industrial Members (20 Companies across the world supporting the Smart Lighting Vision)

24 Thanks Smart Lighting Lighting systems that Think