top of page
Seat Plus is an intelligent cushion that continuously monitors users' sitting posture and adapts to different body shapes by an AI algorithm based on pressure sensor data. It supports the user's lumbar while automatically correcting the spine whenever it deviates from an S-curve alignment by using an air-bag system.
During my 6-month internship at Lenovo Research, I worked with talented people in the user experience design team to create this prototype. My role as an Industrial designer was to help our team develop prototypes based on research and tests.
Sponsorship
Timeline
Team
Lenovo
2018
Lenovo UXD Team
People working at tech companies are more likely to have spine problems such as scoliosis than others due to prolonged sitting time and lack of exercise. A decent office chair can help a lot, but it is still challenging to keep a correct sitting posture.
As a tech company, can Lenovo build a chair or seat that knows users' sitting postures and help them adjust unbalanced sitting postures intuitively?
After several months of exploration, The User Experience Design team at Lenovo Research created this prototype to prove our concept:
A Positive Posture Adjustment System that can sense users' postures and help them adjust actively
Posture Sensor Group
Eight pressure sensors are arranged on the seat board and covered with a bridge. The bridge can optimize the pressure on each sensor for better sitting posture recognition.
Lumbar Support Airbags
Four independent airbags are placed on the lower back area to provide additional support that reduces spinal disc herniating when the user is hunching back.
Pelvis Adjusting Board
The seat board can rotate 7 degrees to improve pelvis angle and change spine curvature more naturally.
This feature was removed in later iterations.
Mock-ups and user testing for learning how to recognize sitting postures by sensors and how to adjust sitting posture from external.
Sensing the sitting posture only by limited sensors is not reliable, so we designed the bridge to collect pressure and distribute it to sensors more evenly. Therefore, we can collect better data to train the algorithm.
I learned some basic Arduino programming from this project by electrical engineers in the team. I had a wonderful time working with them and used Arduino for my later works.
The working prototype we finally developed.
bottom of page