Lux
Modular, Generative, and Human Centered Task Track Lighting
Background
Artificial lighting is an important part of the indoor environment. Designers have faced challenges when designing lighting, and the process is usually more technical than creative. There are online tools that can make the process easier, but most do not consider human behavior which is what this product aims to achieve by incorporating a generative design aspect.
Before coming to a problem statement, here are some of the research findings that helped get us there.
Lighting and human performance
Lighting is essential for human performance — both the color and brightness can effect mood, and adaptability is necessary to accommodate for different space and activities.
Universal design for inclusive lighting
Principles of universal design state that designs should be flexible in use, be simple and intuitive, have perceptible information, have tolerance for error, and require low physical effort. These principles aid in the consideration all people with different levels of visual/physical ability.
Lighting requirements and standards
There is no one set of standards that determine absolute lux values, but there are general guidelines that describe ranges of lux values a space should have in order to optimize lighting.
Artificial Intelligence in lighting industry
Users often don’t notice or understand that their lighting systems can and should work better, and their environments are often changing and users develop different requirements. This is where AI has the ability to intercede and reduce the need for human intervention. For designers, a generative process can create self-learning algorithms that can potentially serve as “expert-on-sights,” increasing the efficiency and success of lighting design.
Existing solutions
There are existing solutions for lighting optimization, and they take into account daylighting data, floor plan analysis, and they also have the ability to calculate luminance. But one area that is missing is taking into consideration human behavior, and we find that to be something that is important.
Problem Statement
There is often a misfit between the lighting provided and the lighting necessary to support the tasks occuring in a room. This results in either excessive or insufficient lighting scenarios and an inefficient use of light.
Solution
Our solution is a track lighting system focused on the three following components:
Design & Development
Human-centered
We found there to be a lack of lighting systems on the market that prioritize catering to the behaviors of users. Knowing the wide range of needs that vary from user to user, we find it extremely important to build the foundation of our lighting system on the behaviors, preferences, and needs of the users themselves. Therefore, we suggest usage of behavior mapping as the method for collecting human movement data throughout a space, that can then be used to better inform the design of the lighting system. In this way, we hope to build a tool that has the ability to provide supportive task lighting solutions that are well-fitted to the end user.
Generative
Our prototype is based on a singular space model and layout, but the idea is that if provided with the right inputs, our tool can be applied to any space layout, regardless of size, number of tasks within the space, or number of occupants. This allows for increased flexibility in fitting lights to a space, as well as in the application of our tool to a variety of different contexts, while minimizing costs in terms of manual work, time, expertise, error, and misfit.
Modular
The system is made up of a series of modular track pieces with different curves, that can be taken apart and put back together in a different formation. Especially due to the dynamic nature of a user’s lighting needs, this feature accommodates the ability to adapt the lighting system to the evolving needs of the user. Additionally, a modular system means increased product lifetime and use, and decreased costs.
The Grasshopper 3D algorithm we used to develop our tool, contains two main components of 1) developing the shape of the track and 2) finding the optimal light setting for the track. The flow chart below shows the inputs and outputs of the algorithm, and also breaks down the algorithm’s process.
To conceptualize our algorithm we wanted to start with a small case, so we choose a studio apartment. When living in a studio apartment, there are a lot of tasks being performed in a single space, and the one or two lights provided in the space is not enough to support all these tasks. So, in our base case, we identified four distinct areas/tasks that require different lighting requirements: kitchen, desk, closet, and bed.
The heat maps visualize the lighting effect of the different light setting based on the task and location within the space.
Heat map visualization of lighting effect of different light settings (kitchen, desk, closet, and bed pictured from left to right)
Machine Learning Through Mobile App
Another quality of the lighting system that we see being implemented in the future, is incorporating machine learning and artificial intelligence. After installation, the lighting system is automatically set to standard settings, however, we recognize that lighting needs vary from user to user. Incorporation of AI can help in accommodating the lighting needs of as many users as possible.
Through an app, the user is able to have manual control over the lighting system, adjusting lighting levels to settings that best suit them for different tasks. By reading this user input over a period of time, our lighting system would learn the preferences of the user and potentially gain the ability to predict user behavior and preference.
mobile and desktop view of preliminary design for the task track light
As generative design tools become more commonplace, designers will also have to adjust to incorporate these tools and begin creating their own systems to make designing easier. Soon, we will not be painstakingly drawing each wall and window, but having artificial intelligent systems generating the next best space. However, this future is far from reality, and we must take small steps to develop such a smart system. As designers, we hope that our algorithm can help to broaden the generative lighting design solutions