Augmenting Smart Buildings and Autonomous Vehicles with Wearable Thermal Technology

Smart buildings and autonomous vehicles are expected to see rapid growth and adoption in the coming decades. Americans spend over 90% of their lives in buildings or automobiles, meaning that 90% of their lives could be spent interfacing with intelligent environments. EMBR Labs has developed EMBR WaveTM, a wearable thermoelectric system, for introducing thermal sensation as a connected mode of interaction between smart environments and their occu-pants. In this paper we highlight applications of wearable thermal technology for passengers in autonomous vehicles and occupants of smart buildings. Initial find-ings, collected through partnerships with Draper and UC Berkeley, respectively, are presented that illustrate the potential for wearable thermal technology to im-prove the situational awareness of passengers in autonomous vehicles and im-prove personal comfort in smart buildings

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies

Adjoint-based optimization of displacement ventilation flow

We demonstrate the use of the ‘Direct-Adjoint-Looping method’ for the identification of optimal buoyancy-driven ventilation flows governed by Boussinesq equations. We use the incompressible Reynolds-averaged Navier-Stokes (RANS) equations, derive the corresponding adjoint equations and solve the resulting sensitivity equations with respect to inlet conditions. We focus on a displacement ventilation scenario with a steady plume due to a line source. Subject to an enthalpy flux constraint on the incoming flow, we identify boundary conditions leading to ‘optimal’ temperature distributions in the occupied zone. Our results show that depending on the scaled volume and momentum flux of the inlet flow, qualitatively different flow regimes may be obtained. The numerical optimal results agree with analytically obtained optimal inlet conditions available from classical plume theory in an ‘intermediate’ regime of strong stratification and two-layer flow

Human Dimensions Of Building Performance: Sensing, Modeling, And Predicting Indoor Environmental Quality

The indoor environment critically affects occupant health and comfort, especially since humans spend most of the day indoors. Meanwhile, occupant activities, preferences, and behaviors may contribute to a significant amount of building energy consumption. The focus of environmental buildings shifted from automated systems to a paradigm of collective environmental design since the second half of the 20th century, emphasizing human dimensions in building performance, which allows occupants to participate as active/passive actuators and sensors. Concurrently, increased environmental awareness further spurred the green building movement intending to encourage more high-performance buildings. The question remains as to whether high-performance buildings are also healthy buildings. This dissertation aims to cast new light on how environmental design and building systems work for people as well as how building sensors and human senses work together to inform the organization and optimization of various performance targets such as sustainability, public health, and resiliency. Special attention is given to the non-visual environment attempting to facilitate human-in-the-loop of the building design and operation processes. In order to achieve this goal, environmental monitoring, data analysis, and human subject recruitments are developed to characterize the human dimension of building performance