A sensing chair using pressure distribution sensors

Abstract

One challenge in multimodal interface research is the lack of robust subsystems that support multimodal interactions. By focusing on a chair-an object that is involved in virtually all human-computer interactions, the sensing chair project enables an ordinary office chair to become aware of its occupant's actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a grayscale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, have been successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar (people it had felt before) and unfamiliar users, respectively. Future work is aimed at a dynamic posture tracking system that continuously tracks not only steady-state (static) but transitional (dynamic) sitting postures. Results reported here form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.

Smart Chair for Monitoring of Sitting Behavior

“…Research studies have been conducted using the pressure distribution measurement sheets placed on the seat pan and backrest to provide high resolution pressure data for posture recognition [4,5]. Tan [4] used principal component analysis to solve the problem of sitting posture classification.…”

“…Research studies have been conducted using the pressure distribution measurement sheets placed on the seat pan and backrest to provide high resolution pressure data for posture recognition [4,5]. Tan [4] used principal component analysis to solve the problem of sitting posture classification. Zhu [5] investigated the classification algorithms and found that Fisher-Rao's discriminant analysis can be applied for the application of sitting posture classification.…”

Template-based Recognition of Static Sitting Postures

“…Two important challenges in the area of multimodal interfaces are: i) to identify new potential modalities that can be effectively used to improve our communication with computers, and ii) to design classification algorithms that can be successfully used in several of these modalities. The sensing chair [13,14] has recently shown to have potential toward the first goal. Unfortunately, the second point remains unsolved.…”

“…The database used in this paper is an extension of the one defined in [14]. The new database consists of 10 different postures: (1) seated upright, (2) leaning forward, (3) right leg crossed, (4) left leg crossed, (5) leaning back, (6) leaning left, (7) leaning left with right leg crossed, (8) leaning right, (9) leaning right with left leg crossed, and (10) slouching.…”

“…The sitting posture data captured with the sensing chair has been found to be extremely challenging for most of the pattern recognition algorithms defined in the literature. To our knowledge, a simple Euclidean distance in the reduced space of the Principal Components of the covariance matrix of the pressure map is currently the best and most robust system designed to date [14] where results are around 85% for known users and below 75% for unknown users.…”