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Z-Tiles: Building Blocks for Modular, Pressure-Sensing Floorspaces Bruce Richardson, Krispin Leydon, Mikael Fernström, Joseph A. Paradiso

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Presentation on theme: "Z-Tiles: Building Blocks for Modular, Pressure-Sensing Floorspaces Bruce Richardson, Krispin Leydon, Mikael Fernström, Joseph A. Paradiso"— Presentation transcript:

1 Z-Tiles: Building Blocks for Modular, Pressure-Sensing Floorspaces Bruce Richardson, Krispin Leydon, Mikael Fernström, Joseph A. Paradiso

2 Introduction New Pressure- Sensitive Floorspace Successor to Litefoot (1998) & Magic Carpet (1997) Paradiso et al. (1997) The Magic Carpet: Physical Sensing for Immersive Environments. CHI97 Griffith and Fernstrom (1998) LiteFoot: A Floor Space for Recording Dance and Controlling Media. ICMC98.

3 Authors Bruce Richardson Interaction Design Centre, UL. Krispin Leydon Interaction Design Centre, UL. Mikael Fernström Interaction Design Centre, UL. Joe Paradiso MIT Media Lab, Boston

4 Requirement Scalable => Modular and interchangable

5 Requirement Managable => Self-organising and reconfigurable

6 Requirement Real-Time => Fast scanning and fast output Scan at 100 Hz Low latency data routing Minimal network overhead

7 Sensor Units

8 Z-Tile Circuitry

9 Z-Tile Architecture

10 Floor Prototype

11 Data Bottleneck Many wires vs. One wire Bottleneck at connection point

12 Options 1. Output only changed pressure readings 2. Group similar pressure readings

13 Options 1. Output only changed pressure readings 2. Group similar pressure readings

14 Blob Matching

15 Considerations 100 pressure scans/sec 10ms maximum computation time Minimum number of parameters Accurate blob matching Trade off Close fit vs Fewer parameters

16 Ellipse Matching Circles: Loose fit, few parameters Polygons: Tight fit, many parameters

17 Ellipses Ellipses - good compromise

18 Simple Matching

19 Average -> Centre

20 Axes -> Bounding Box

21 Resultant Ellipse

22 Results Pressures distilled to blobs 5 parameters per blob Implemented on a microcontroller >800 scans processed per second Rate achieved at 1/10 speed

23 Evaluation Processing time to spare Difficulty with diagonal blobs Therefore, look for better match

24 Angled Ellipses Pressure ReadingsWe have We want

25 General Method Determine centre as before Locate 2 most distant points Set this as major axis

26 General Method contd Rotate points so axis is horizontal Use bounding box to determine major and minor axes lengths

27 Results Implemented on microcontroller Integer-only calculations Lookups for rotations Computation time 1- 2ms

28 Summary Blob detection to reduce data 2 ellipse-matching algorithms Run time on hardware: <2ms

29 Future Work Integrate blob detection algorithm into tile software Detect blobs across tiles

30 Questions?


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