Client: Intuition Robotics Date: January 2018
Intuition Robotics is developing a social companion technology to positively impact the lives of millions of older adults by connecting them seamlessly with family and friends, making technology accessible and intuitive, and proactively promoting an active lifestyle.
ELLI•Q™ is an active aging companion that keeps older adults active and engaged. ELLI•Q™ seamlessly enables older adults to use a vast array of technologies, including video chats, online games, and social media to connect with families and friends and overcome the complexity of the digital world.
Device height: 270 mm
Device width: 375 mm
Device depth: 235 mm
ELLI•Q™ inspires participation in activities by proactively suggesting and instantly connecting older adults to digital content such as TED talks, music, or audio books. The system recommends activities in the physical world like taking a walk after watching television for a prolonged period, keeping appointments, taking medications on time, and connecting with family members.
The strong idea that rests at the core of the ELLI•Q concept is to create an enchanting connection between the intended user and the device. To do so, many features have been packed into ELLI•Q’s compact shell. Features include audio instrumentation, visual components, spatial awareness, computer vision, and animated motion expressing “body language”.
To create the illusive connection between man and machine, the designers chose to create ELLI•Q in abstract form rather than humanoid.
Firstly, the challenge is to effectively package all of ELLI•Q’s features into the tightly packed, beautifully designed shell. Secondly, the electro-mechanical challenge is animating ELLI•Q with non- robotic gestures. This includes ultra-smooth motion, quiet operation, no jittering, or vibration associated with traditional robotic systems. Additional challenges include communicating and powering the different sections of the robot while keeping all lines hidden, maintaining the extreme aesthetics of the shells.
When it comes to the design of plastic parts, the shapes and surfaces creating each component had to be carefully designed. Smooth, continuous contours define the outline of every geometry in the design.
Light manipulation plays a key role in ELLI•Q’s self-expression and presents one of the major challenges in the design. ELLI•Q’s creators could not compromise on this feature, leading to intricate lighting schemes integrated seamlessly into the design contributing to ELLI•Q’s range of reactions.
Putting all factors in conjunction with minimalist ID requirements such as little to no seems as possible in the assembly, challenged the mechanical and electronics teams to create innovative parts and assemblies pushing the envelope of technological boundaries.
Using “Natural Communication” such as body language that conveys emotion, speech interface, sounds, lights, and images to express herself, ELLI•Q is emotive, autonomous, and easily understood. Using machine learning, she learns the preferences, behavior, and personality of her owner, and proactively recommends activities based on its learning and based on recommendations by family. ELLI•Q also can monitor wellness and the environment in the home.
To design all of ELLI•Q’s components, the design team used state of the art tools. The entire assembly was designed with SolidWorks, with careful attention to details, implementing vital tools to produce parts for high pressure plastic molding, aluminum dye casting and sheet metal stamping.
Advanced analysis tools were used for calculating the dynamic, static, and reaction forces involved in ELLI•Q’s motions, optimizing motor systems. Innovative engineering was practiced through various components and assemblies to allow the use of cost effective, off-the-shelf components.
Rendering tools were used to better understand light diffraction through plastic translucent parts and light guides. Diffusions, reflections, and shaded zones were identified and addressed in early stages of the project. Later, light modules were modeled in rapid prototyping and verified in actual testing.