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How to Detect soft falls
on devices
some signal processing with knime”
Prof. Dr. Dominique Genoud
Institute of Information Systems
Dominique.Genoud@hevs.ch
Techno-Pôle 3 – CH-3960 Sierre
Switzerland
Vincent Cuendet master HES-SO,
Lausanne, Switzerland
Julien Torrent FST,
Neuchâtel,Switzerland
Processing flow
Connected android watches
Moto 360 and LG-G watches
Use the magnitude of 3D vector
Falls
t
About 98% detection
Soft falls
[ms]
1% detection with
thresholds
Signal processing to find patterns
• Decomposition of a temporal signal:
Fast Fourrier Transform
(FFT)
In the digital word :
Discrete Fourrier Transform
(DFT)
Knime to perform DFT
Reading the samples, and
computing the magnitude
vector
Knime to perform DFT
Knime to perform DFT
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 :
Other polular pattern detection
• Wavelet transform :
https://en.wikipedia.org/wiki/Wavelet_transform
Knime to perform Wavelets
• Implemented in the jwave.jar library:
Haare Legendre Daubechie
(db2)
Knime to perform Wavelets
Processing flow
Classification experiments
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
Scoring and precision of AUC
How good are
the Decision Tree Ensemble
Processing flow
Implementation on Android
Jpmml - open source
library
On existing App
Enhancements
• Pmml to java compiler
• Wrapped nodes
• Optimize the size of the pmml in memory
References
• The example workflows will be available
• Paper on soft falls
Questions?
24

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