Transcript of Kai Kunze and Paul Lukowicz Embedded Systems Lab, University of Passau, Insstr 43, 94032 Passau,...
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- Kai Kunze and Paul Lukowicz Embedded Systems Lab, University of
Passau, Insstr 43, 94032 Passau, Germany UbiComp 2007
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- Outline 1. Introduction 2. Approach Overview 3. Recognition
Method 4. Experimental Validation 5. Conclusion
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- 1.Introduction Present and systematically evaluate a novel
method for object localization. The method provides so called
symbolic location rather then absolute coordinates. The key
contribution : Present a method that requires no infrastructure,
relies on simple, cheap sensors. How to derived the method ? Create
a mechanical excitation of the environment and analyze the response
with an accelerometer and a microphone.
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- 1.Introduction Two types of information can be derived from
this analysis. (1). The system can be trained to recognize specific
locations. (2). It can recognize more abstract locations based on
materials. Advantage: Less specific positioning, the system does
not need to be trained for each single location. Aims at the
localization of simple objects in environments with no, or only
minimal augmentation.
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- 1.Introduction-Related Work Not focus on reliable, standard
method. Ultrasonic Location instrumentation: BAT, MIT cricket
systems. Require extensive instrumentation of the environment with
ultrasonic transceivers and free line of sight and will fail to
locate objects in closed compartments. Time of flight based radio
frequency (RF) methods: UBISENSE ultra wide band system. Cost and
effort (UWB).
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- 1.Introduction-Related Work Simple Beacon Based Systems:
Localization based on simple RF beacons, Bluetooth, Zigbee and
WLAN, RF based system. Knowing approximate physical location can be
used to constrain the search space. Indirect Localization with
Sensor Signatures: Sound and acceleration. General concept of using
acceleration signatures to extract location related
information.
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- 1.Introduction An important feature of their method is the fact
that it can be used on both specific locations (e.g. my kitchen
table), and abstract location types. Provide a brief description of
the recognition algorithm, including, feature computation,
classification, and classifier fusion. Data set contains a total of
over 1200 measurements from 35 specific locations (taken from 3
different rooms) and 12 abstract location classes.
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- 1.Introduction Organization On room bases (16, 9 and 10
locations) we arrive at an accuracy of between 89% and 93 % with
the correct answer being in the to 2 first picks of the classifier
between 97 % and 99 % of the time. With all 35 locations from the 3
rooms in one data set the recognition goes down to 81 %. However we
still get the correct answer in the top 2 picks of the
classifier.
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- 2.Approach Overview-The Method Procedure Description (Proposed
method consists of two parts): Part 1. Based on vibrating the
device using a vibration-motor of the type commonly found in mobile
phones. Motion and sound signals are used for an initial location
classification using standard feature extraction and pattern
recognition methods. Final classification is obtained through
appropriate fusion of the two classification results.
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- 2.Approach Overview-The Method Part 2. Based on sound sampling.
Emits a series of beeps, each in a different, narrow frequency
spectrum. Receives only little energy directly from the speaker.
Instead a significant part of the energy comes from reflections
from the immediate environment. Two parts are used together, the
corresponding results are fused using an appropriate classifier
fusion method.
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