Mean Shift, Mode Seeking, and Clustering
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Z-Tiles: building blocks for modular, pressure-sensing floorspaces
CHI '04 Extended Abstracts on Human Factors in Computing Systems
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AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
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CHI EA '97 CHI '97 Extended Abstracts on Human Factors in Computing Systems
A pressure sensing floor for interactive media applications
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
People Identification Using Gait Via Floor Pressure Sensing and Analysis
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
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This paper addresses the design of a large area, high resolution, networked pressure sensing floor with primary application in movement-based human-computer interaction (M-HCI). To meet the sensing needs of an M-HCI system, several design challenges need to be overcome. Firstly, high frame rate and low latency are required to ensure real-time human computer interaction, even in the presence of large sensing area (for unconstrained movement in the capture space) and high resolution (to support detailed analysis of pressure patterns). The optimization of floor system frame rate and latency is a challenge. Secondly, in many cases of M-HCI there are only a small number of subjects on the floor and a large portion of the floor is not active. Proper data compression for efficient data transmission is also a challenge. Thirdly, locations of disjoint active floor regions are useful features in many M-HCI applications. Reliable clustering and tracking of active disjoint floor regions poses as a challenge. Finally, to allow M-HCI using multiple communication channels, such as gesture, pose and pressure distributions, the pressure sensing floor needs to be integrable with other sensing modalities to create a smart multimodal environment. Fast and accurate alignment of floor sensing data in space and time with other sensing modalities is another challenge. In our research, we fully addressed the above challenges. The pressure sensing floor we developed has a sensing area of about 180 square feet, with a sensor resolution of 6.25 sensels/in2. The system frame rate is up to 43 Hz with average latency of 25 ms. A simple but efficient data compression scheme is in place. We have also developed a robust clustering and tracking procedure for disjoint active floor regions using the mean-shift algorithm. The pressure sensing floor can be seamlessly integrated with a marker based motion capture system with accurate temporal and spatial alignment. Furthermore, the modular and scalable structure of the sensor floor allows for easy installation to real rooms of irregular shape. The pressure sensing floor system described in this paper forms an important stepping stone towards the creation of a smart environment with context aware data processing algorithms which finds extensive applications beyond M-HCI, e.g. diagnosing gait pathologies and evaluation of treatment.