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Recognition of Human Physical Activity based on a novel Hierarchical Weighted Classification scheme

The analysis of daily living human behavior has proven to be of key importance to prevent unhealthy habits. The diversity of activities and the individuals’ particular execution style determine that several sources of information are normally required. One of the main issues is to optimally combine them to guarantee performance, scalability and robustness. In this paper we present a fusion classification methodology which takes into account the potential of the individual decisions yielded at both activity and sensor classification levels. Particularly tested on a wearable sensors based system, the method reinforces the idea that some parts of the body (i.e., sensors) may be specially informative for the recognition of each particular activity, thus supporting the ranking of the decisions provided by each associated sensor decision entity. Our method systematically outperforms the results obtained by traditional multiclass models which otherwise may require a high-dimensional feature space to acquire a similar performance. The comparison with other activity-recognition fusion approaches also demonstrates our model scales significantly better for small sensor networks. This presentation illustrates part of the work described in the following articles: * Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B. & Valenzuela, O. Human activity recognition based on a sensor weighting hierarchical classifier. Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin / Heidelberg, vol. 17, pp. 333-343 (2013) * Banos, O., Damas, M., Pomares, H., Rojas, I.: Recognition of Human Physical Activity based on a novel Hierarchical Weighted Classification scheme. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN 2011), IEEE, San José, California, July 31- August 5, (2011)

1 of 14
Recognition of Human Physical
Activity based on a novel
Hierarchical Weighted Classification
scheme
IJCNN 2011
San José, California (USA)
O. Banos, M. Damas, H. Pomares, I. Rojas
Department of Computer Architecture and
Computer Technology (University of Granada)
Work supported in part by the Spanish CICYT Project TIN2007-60587,
Junta de Andalucia Projects P07-TIC-02768 and P07-TIC-02906, the
CENIT project AmIVital and the FPU Spanish grant AP2009-2244
Agenda
Introduction
Experimental setup
Methods
Results
Conclusions
05/05/2014
2
Activity Recognition
• Fundamental part of medical/health assistant
work, being applicable to other areas (sport
efficiency, videogames industry, robotics, etc.)
• Changeableness due to the capability to discover
and identify actions, movements and gestures
than normally are unnoticed
• Objectives
05/05/2014
3
 Define an original methodology
 Identify the main characteristics
 Improve state of the art results through efficient
and accurate knowledge inference algorithms
Experimental setup
Fiveaccelerometers
Walking
Sitting and
relaxing
Standing still
Running
Bicycling
Lying down
Brushing teeth
Climbing stairs
05/05/2014
4
Eightactivities
Data preprocessing
• Different approaches were studied
• Best results “a posteriori” using a LPF+HPF (IIR
elliptic)
05/05/2014
5
ORIGINAL MEAN FILTERING LPF+HPF
Feature extraction
Magnitudes
Amplitude
Autocorrelation function
Cepstrum
Correlation lags
Cross correlation function
Energy spectral density
Spectral coherence
Spectrum amplitude/phase
Histogram
Historical data lags
Minimum phase reconstruction
Wavelet decomposition
Statistical functions
4th and 5th central statistical moments
Energy
Arithmetic/Harmonic/Geometric/ Trimmed mean
Entropy
Fisher asymmetry coefficient
Maximum / Position of
Median
Minimum / Position of
Mode
Kurtosis
Data range
Total harmonic deviation
Variance
Zero crossing counts
05/05/2014
6

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Recognition of Human Physical Activity based on a novel Hierarchical Weighted Classification scheme

  • 1. Recognition of Human Physical Activity based on a novel Hierarchical Weighted Classification scheme IJCNN 2011 San José, California (USA) O. Banos, M. Damas, H. Pomares, I. Rojas Department of Computer Architecture and Computer Technology (University of Granada) Work supported in part by the Spanish CICYT Project TIN2007-60587, Junta de Andalucia Projects P07-TIC-02768 and P07-TIC-02906, the CENIT project AmIVital and the FPU Spanish grant AP2009-2244
  • 3. Activity Recognition • Fundamental part of medical/health assistant work, being applicable to other areas (sport efficiency, videogames industry, robotics, etc.) • Changeableness due to the capability to discover and identify actions, movements and gestures than normally are unnoticed • Objectives 05/05/2014 3  Define an original methodology  Identify the main characteristics  Improve state of the art results through efficient and accurate knowledge inference algorithms
  • 4. Experimental setup Fiveaccelerometers Walking Sitting and relaxing Standing still Running Bicycling Lying down Brushing teeth Climbing stairs 05/05/2014 4 Eightactivities
  • 5. Data preprocessing • Different approaches were studied • Best results “a posteriori” using a LPF+HPF (IIR elliptic) 05/05/2014 5 ORIGINAL MEAN FILTERING LPF+HPF
  • 6. Feature extraction Magnitudes Amplitude Autocorrelation function Cepstrum Correlation lags Cross correlation function Energy spectral density Spectral coherence Spectrum amplitude/phase Histogram Historical data lags Minimum phase reconstruction Wavelet decomposition Statistical functions 4th and 5th central statistical moments Energy Arithmetic/Harmonic/Geometric/ Trimmed mean Entropy Fisher asymmetry coefficient Maximum / Position of Median Minimum / Position of Mode Kurtosis Data range Total harmonic deviation Variance Zero crossing counts 05/05/2014 6
  • 7. Why feature selection is needed? • Influence on classification process OPTIMUM Few Features Good classification 0 500 1000 -1 -0.5 0 0.5 1 x 10 4 Thigh accelerometer Features Featurevalue 05/05/2014 7 • Huge feature set (861 parameters  2861  1.5 x 10259 possible combinations) Mann-Whitney- Wilcoxon FS
  • 8. Binary vs. multiclass classification 05/05/2014 8 • # of classes discriminated • Binary classifiers are more accurate in general than direct multiclass classifiers • Problem: define an adequate multiclass extension scheme • Depends on the particular experiment • No general models for multisource problems 2  SVM, NB ≥2  DT
  • 9. Hierarchical weighted classifier (HWC) 05/05/2014 9 N classes     NqxDxO mknq N n mnmkmq ,...,1 1     )(maxarg mkmq q m xOq    N k mk mn mn R R 1    M k k m m R R 1  NqxOxxOxO pkpq M p pMkkqkq ,...,1)(}),...,({)( 1 1      ],...,1[)(maxarg NqxOq kq q  M sources&
  • 10. Results 05/05/2014 10 • Naïve Bayes • 10-fold cross validation • N=8, M=5 70,00 80,00 90,00 100,00 Hip Wrist Arm Ankle Thigh Fusion Classificationaccuracy(%) Using 1 feature for eachclass classifier MV HWC 70,00 80,00 90,00 100,00 Hip Wrist Arm Ankle Thigh Fusion Classificationaccuracy(%) Using 10 features for eachclass classifier MV HWC
  • 11. Comparison with other studies 05/05/2014 11 Work Accuracy rates S.W. Lee and K. Mase. Activity and location recognition using wearable sensors. 92.85% to 95.91% J. Mantyjarvi, J. Himberg, and T. Seppanen. Recognizing human motion with multiple acceleration sensors. 83.00% to 90.00% K. Aminian, P. Robert, E. E. Buchser, B. Rutschmann, D. Hayoz, and M. Depairon. Physical activity monitoring based on accelerometry: validation and comparison with video observation. 89.30% L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions 89.00% THIS WORK 97.08% (1 feat.), 97.81% (10 feat.) Source: L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions
  • 12. Conclusions • The hierarchical system defined only requires: ▫ Binary classifiers based on individual features with high binary discriminant capability ▫ A few weighting parameters and simple decision rules • According to the particular activity recognition model: ▫ HWC offers better results for both source and fusion classification approaches  Improvement up to 15% with respect to MV  Similar good results when fusion for both 1 and 10 features are used  Particular good results for the wrist or arm data based source classifier (≈96%) 05/05/2014 12
  • 13. Future work • Test non-linear subclassifiers combinations • Include a probability of membership to the different classes besides the current weighted scheme • Analyze other multiclass extensions and compare with HWC performance • Spread the study to a larger number of activities (classes) • Apply this methodology to other kind of problems 05/05/2014 13
  • 14. Thank you for your attention Questions? 05/05/2014 14 Oresti Baños Legrán Dep. Computer Architecture & Computer Technology Faculty of Computer & Electrical Engineering (ETSIIT) University of Granada, Granada (SPAIN) Email: oresti@atc.ugr.es, oresti@ugr.es Phone: +34 958 241 516 Fax: +34 958 248 993