Acceleration Based Hci Prototype
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Transcript of Acceleration Based Hci Prototype
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Acceleration Based HCI Prototype
Peng Deng: 263497 Qifeng Mao: 272746
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Characteristics of human motion
Challenges: 1.Recognition; 2. Segmentation
Facts: 1.Multi-attributes; 2. Data stream
Solutions: 1.Machine Learning; 2. Threshold+std dev
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Characteristics of human motion cont.
Challenges: 1.Recognition; 2. Segmentation
Facts: 1.Multi-attributes; 2. Data stream
Solutions: 1.Machine Learning; 2. Threshold+std dev
![Page 4: Acceleration Based Hci Prototype](https://reader036.fdocuments.net/reader036/viewer/2022082921/55630e42d8b42a4b1d8b5489/html5/thumbnails/4.jpg)
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Architecture
Sensor-Side Host-Side
Tomcat Axis
(Sun SPOTs)
Human Gesture
Phone Call WS
· SVGService.jws
Base Station
Data Stream Filter
Segmentation
Module
HMM Engine
SVG Application
· zoom in· zoom out· Zoom X axis· Zoom Y axis· clock rotate· reverse clock rotate
· Web Calls
Soft Phone
IAX2
VOIP PBX
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Extension 2000
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Server: Trixbox.nikdow.net
SocketsS
AO
PRadiogram
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DEMO
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Auto segmentation
a b s
Sliding Window: common method to process data stream e.g. TCP, DSMS
Standard Deviation: How stable the data series is
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