Purdue engineers develop a chair with 'sense'

November 01, 2000

WEST LAFAYETTE, Ind. -- Purdue University engineers have developed a "sensing chair" that can determine a person's sitting posture, research that could lead to numerous applications, from computer-security systems to the design of more comfortable furniture.

The modified office chair uses software algorithms, or computer instructions, that interpret information collected by an array of pressure sensors in the backrest and seat. When tested on 30 people, the chair demonstrated an overall accuracy of 96 percent in determining whether people were slouching, leaning in various positions, crossing their legs or sitting upright.

"The chair senses how the pressure is distributed in the seat and the backrest," said Hong Tan, an assistant professor at Purdue's School of Electrical and Computer Engineering. "We train the computer to recognize pressure patterns associated with different seating postures by showing the computer examples of such patterns."

Lynne A. Slivovsky, a doctoral student working with Tan, will present a research paper about the work Nov. 9 during the 2000 International Mechanical Engineering Congress & Exposition, sponsored by the American Society of Mechanical Engineers in Orlando, Fla.

Special software enables a computer to interpret a person's posture by analyzing pressure patterns, which are represented by thousands of numbers fed to the computer by numerous sensing elements, or "sensels," in the chair. Each time a person sits in the chair, the computer creates precise "pressure maps" that can distinguish between different people, even if they are sitting in the same position.

The system is limited in that it is capable only of sensing "static posture," or how a person is sitting at any one given time.

"Currently, we are working on a dynamic system so that we can see how people are moving, throughout an eight-hour day, for example," Tan said.

Such an advanced "real-time sitting posture tracking system" would lead to many applications. Because the system would be able to recognize the pressure patterns peculiar to specific people, a potential application might be to verify authorized personnel for computer-security purposes.

A sensing chair also might be used in cars to automatically adjust the driver's seat according to who is behind the wheel, or to control an airbag's deployment by adjusting for a person's seating position and weight.

Another potential application could be to improve the ergonomics of furniture.

"People come into the showroom, they sit down, they think, 'Oh, this chair feels great,'" Tan said. "They buy it, and then they return it because after several hours of sitting in it, it doesn't feel great any more.

"So, chair manufacturers are interested in how to evaluate a chair over an extended period of time. They want to understand the long-term dynamics of seating."

The chair is most adept at sensing when someone is slouching, correctly interpreting that posture with an accuracy of 99.8 percent.

"For anybody who wants to do anything related to ergonomics, slouching is the one posture you probably really want to discourage," said Tan, who specializes in research focusing on the haptic human-machine interface, or how machines and people interact through the sense of touch.

Perhaps such a sensing chair might sound a warning beep every time its user assumed a slouching posture, she said.

Researchers said they were surprised the chair was able to accurately distinguish between the subtle posture differences of leaning left, crossing the right leg while leaning left or crossing the right leg without leaning at all.

"We were expecting to see a lot of confusion among those three because they are so similar," Tan said.

Tan and Slivovsky will present the findings during the engineering conference's Symposium on Haptic Interfaces for Virtual Environments and Teleoperation, sponsored by the mechanical engineering society's Dynamic Systems and Control Division.
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esv/Tan.Smartchair

Writer: Emil Venere, (765) 494-4709, evenere@uns.purdue.edu

Related Web sites:
Hong Tan's Web page: http://www.ece.purdue.edu/~hongtan/
Lynne Slivovsky's Web page: http://rvl1.ecn.purdue.edu/~lynnes/
2000 International Mechanical Engineering Congress & Exposition: http://www.asme.org/conf/congress00/index.htm

Purdue University
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STORY AND PHOTO CAN BE FOUND AT: http://news.uns.purdue.edu/html4ever/001103.Tan.smartchair.html

NOTE TO JOURNALISTS: An electronic version of the research paper referred to in this release is available from Emil Venere, (765) 494-4709, evenere@uns.purdue.edu. A publication-quality photograph of doctoral student Lynne Slivovsky using the chair is available at http://news.uns.purdue.edu or at ftp://ftp.purdue.edu/pub/uns/. It is called Tan.Smartchair.

Sources:
Hong Z. Tan, (765) 494-6416, hongtan@ecn.purdue.edu
Lynne A. Slivovsky, (765) 494-3453, lynnes@ecn.purdue.edu

ABSTRACT

A Real-Time Static Posture Classification System

Lynne A. Slivovsky, Hong Z. Tan

Haptic Interface Research Laboratory

School of Electrical and Computer Engineering

Purdue University

As computing becomes more ubiquitous, there is a need for distributed intelligent human-computer interfaces that can perceive and interpret a user's actions through sensors that see, hear and feel. A perceptually intelligent interface enables a more natural interaction between a user and a machine in the sense that the user can look at, talk to or touch an object instead of using a machine language. The goal of the present work on a Sensing Chair is to enable a computer to track, in real time, the sitting postures of a user through contact sensors that act like a layer of artificial skin. This is accomplished with surface-mounted pressure distribution sensors placed on the backrest and the seatpan of an office chair. Given the similarity between a pressure distribution map from the contact sensors and a greyscale image, computer vision and pattern recognition algorithms, such as Principal Components Analysis, are applied to the problem of classifying steady-state sitting postures. A real-time multi-user sitting posture classification system has been implemented in our laboratory. The system is trained on pressure distribution data from subjects with varying anthropometrics, and performs at an overall accuracy of 96 percent. Future work will focus on the modeling of transient postures when a user moves from one steady-state posture to the next. A robust, real-time sitting posture tracking system can lead to many exciting applications such as automatic control of airbag deployment forces, ergonomics of furniture design and biometric authentication for computer security.

Purdue University

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