At the Knime Berlin summit 2016, Prof. Dr. Dominique Genoud presented a novel way to implement a KNIME workflow that perform machine learning and signal processing on an Android platform. The use case was to detect soft falls (not from a standing position) using an Android watch. This application has a big impact on how we can detect automatically when elderly people fall from their bed of their chair. This work was originally based on the Master Thesis in Business Administration realized by Vincent Cuendet in 2015 at the HES-SO with the help of the FST (Fédération Suisse pour les Téléthèses), an organization that helps disabled and elderly people to keep their autonomy.

1. 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

2. Processing flow

3. Connected android watches
Moto 360 and LG-G watches

4. Use the magnitude of 3D vector

5. Falls
t
About 98% detection

6. Soft falls
[ms]
1% detection with
thresholds

7. Signal processing to find patterns
• Decomposition of a temporal signal:
Fast Fourrier Transform
(FFT)
In the digital word :
Discrete Fourrier Transform
(DFT)

8. Knime to perform DFT
Reading the samples, and
computing the magnitude
vector

9. Knime to perform DFT

10. Knime to perform DFT

11. 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 :

12. Other polular pattern detection
• Wavelet transform :
https://en.wikipedia.org/wiki/Wavelet_transform

13. Knime to perform Wavelets
• Implemented in the jwave.jar library:
Haare Legendre Daubechie
(db2)

14. Knime to perform Wavelets

15. Processing flow

16. Classification experiments

17. 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

18. Scoring and precision of AUC

19. How good are
the Decision Tree Ensemble

20. Processing flow

21. Implementation on Android
Jpmml - open source
library
On existing App

22. Enhancements
• Pmml to java compiler
• Wrapped nodes
• Optimize the size of the pmml in memory

23. References
• The example workflows will be available
• Paper on soft falls

24. Questions?
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