Ho How w to to Detec Detect soft soft falls alls on on de - - PowerPoint PPT Presentation

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Ho How w to to Detec Detect soft soft falls alls on on de - - PowerPoint PPT Presentation

Jun 04, 2023 189 likes •440 views

Ho How w to to Detec Detect soft soft falls alls on on de devices vices some signal processing with knime Prof. Dr. Dominique Genoud Vincent Cuendet master HES-SO, Institute of Information Systems Lausanne, Switzerland

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Ho How w to to Detec Detect soft soft falls alls

  • n
  • n

de devices vices

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

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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?

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