how to detect soft falls on devices
TRANSCRIPT
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How to Detect soft falls
on devices
some signal processing with knime”
Prof. Dr. Dominique Genoud
Institute of Information Systems
Techno-Pôle 3 – CH-3960 Sierre
Switzerland
Vincent Cuendet master HES-SO,
Lausanne, Switzerland
Julien Torrent FST,
Neuchâtel,Switzerland
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Processing flow
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Connected android watches
Moto 360 and LG-G watches
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Use the magnitude of 3D vector
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Falls
t
About 98% detection
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Soft falls
[ms]
1% detection with
thresholds
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Signal processing to find patterns
• Decomposition of a temporal signal:
Fast Fourrier Transform
(FFT)
In the digital word :
Discrete Fourrier Transform
(DFT)
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Knime to perform DFT
Reading the samples, and
computing the magnitude
vector
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Knime to perform DFT
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Knime to perform DFT
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Knime to perform DFT
• Jwave.jar:
– open source library developed by C.
Scheiblich (MIT)
• The source code and examples are
available there:
– https://github.com/cscheiblich/JWave
• License free :
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Other polular pattern detection
• Wavelet transform : https://en.wikipedia.org/wiki/Wavelet_transform
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Knime to perform Wavelets
• Implemented in the jwave.jar library:
Haare Legendre Daubechie
(db2)
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Knime to perform Wavelets
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Processing flow
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Classification experiments
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Results
Preprocessing Predictor AUC precision
FFT 8 coefficients
Decision Tree Ensemble 0.84 ±0.03
Mulltilayer Perceptron 0.79 ±0.03
Wavelets Haare
Decision Tree Ensemble 0.87 ±0.03
K Nearest Neibourghs 0.75 ±0.04
Daubechie 128 coefficients
Decision Tree Ensemble 0.86 ±0.03
K Nearest Neibourghs 0.73 ±0.04
Stratified Subset of 20% of the original data
Full dataset: 500 soft falls and
1500 normal activities
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Scoring and precision of AUC
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How good are
the Decision Tree Ensemble
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Processing flow
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Implementation on Android
Jpmml - open source
library
On existing App
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Enhancements
• Pmml to java compiler
• Wrapped nodes
• Optimize the size of the pmml in memory
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References
• The example workflows will be available
• Paper on soft falls
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Questions?
24