A sensing chair using pressure distribution sensors

One challenge in multimodal interface research is the lack of robust subsys tems 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 occu pant'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 simila rity between a pressure distribution map and a grayscale image, pattern rec ognition techniques commonly used in computer and robot vision, such as pri ncipal components analysis, have been successfully applied to solving the p roblem of sitting posture classification. The current static posture classi fication system operates in real time with an overall classification accura cy of 96% and 79% for familiar (people it had felt before) and unfamiliar u sers, respectively. Future work is aimed at a dynamic posture tracking syst em 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 au tomobile operations.