The SimTigrate Design Center has conducted several projects surrounding sleep and other impacts of light in healthcare spaces. Our early work characterized the functions lighting needs to perform in an in-patient environmentto support quality care delivery. One finding has been that the current design of lighting in these environments is insufficient in achieving these functions.

A recently improved understanding of the physiology of the eye has created new opportunities to use lighting to impact various aspectsof daily life, including alertness and mood. Lighting technology itself also has advanced: we now have access to LED lights that can be tuned and dimmed to specific color temperatures. These factors provide promising potential impacts in healthcare environments.

An excerpt of LAeq measurements from two sound and light sensors and XL2 running alongside each other overnight in a home

TMCity Lighting Research

TMCity Lighting Research or Practical High-Fidelity Sensing of the Sleep Environment in the Home

Keywords: sleep environment; in-home sensing; high-fidelity monitoring; ambient sensors; residential health technology
Methods: Sensor Development, Environmental Monitoring
 

Recent research on the effects of lighting has shown that the spectrum of light and its intensity, duration, and distribution can have important non-visual impacts, such as increasing alertness, improving mood, and helping sleep. However, little research has focused on people with MCI, and we are studying how lighting and noise can impact cognition and alertness for people with MCI. This project aims to evaluate the lighting characteristics of the built environment of the CEP members and to define the possible correlations with sleep disorders for this population.

More specifically, non-invasive monitoring of the sleep environment can improve health outcomes by capturing the exogenous factors that contribute to sleep disruption. In an aging population, where disturbed sleep is a common occurrence, being able to capture minute changes to the lighting and the sound environment throughout the night is critical to obtaining a more holistic view of sleep behaviors and opportunities for interventions. In order to continuously capture relevant sound and light characteristics in a bedroom environment, a robust, low-cost, and scalable sensing system is needed. Existing systems for capturing the sleep environment are untested, inadequate for permanent placement in the bedroom due to size, or lack detail in the collected acoustic and lighting metrics. This research describes a low-cost and robust system designed to collect highly accurate environmental light and sound data in a natural home environment at fine temporal resolution. The performance of the sensing modalities was tested and found to match simultaneous measurements using industry-standard precision instruments closely.

3D rendering of sensor placement in participants bedrooms

Designing and Delivering Inclusive Research

Multimodal In-home Sensing to Link Indoor Environmental Quality to Subjective Sleep Health of Underserved Populations

Keywords: older adults, multimodal sensing, indoor environmental quality (IEQ), sleep environment, health disparities, in-home sensing
Methods: Case Study, Sensors, Data Visualization
 

This case study draws from our experience conducting a multimodal sensing study in two homes to evaluate the relationship between indoor environmental quality (IEQ) and subjective sleep health among African American older adults with Mild Cognitive Impairment (MCI) residing in low socioeconomic communities in Atlanta, GA, USA. The research aimed to determine whether subjective measures of sleep health, assessed using the Pittsburgh Sleep Quality Index and Insomnia Severity Index, are associated with objective measures of temperature, relative humidity, light, and noise in the sleeping environment. 

We describe the method we used to collect objective data, highlighting the ethical and practical approach of conducting sensing studies with an underserved population. We include our planning considerations, such as sensor type and placement selection, and how these decisions were made off-site using LiDAR scans collected during an initial home assessment. Through our approach, we were able to identify the benefits of our method, as well as the learning outcomes of conducting in-home sensing studies with older adults.

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