JACIII Vol.22 No.1 pp. 88-96
doi: 10.20965/jaciii.2018.p0088


A Heterogeneous Ensemble Learning Voting Method for Fatigue Detection in Daily Activities

Lulu Wang*, Zhiwu Huang*,**, Shuai Hao*, Yijun Cheng*, and Yingze Yang*

*School of Information Science and Engineering, Central South University
Changsha City, Hunan Province 410075, China

**Hunan Engineering Laboratory of Rail Vehicles Braking Technology

June 9, 2017
October 13, 2017
January 20, 2018
lower extremity fatigue, gait data, heterogeneous voting method

Lower extremity fatigue is a risk factor for falls and injuries. This paper proposes a machine learning system to detect fatigue states, which considers the different influences of common daily activities on physical health. A wearable inertial unit is devised for gait data acquisition. The collected data are reorganized into nine data subsets for dimension reduction, and then preprocessed via gait cycle division, visualization, and oversampling. Then, a heterogeneous ensemble learning voting method is employed to train nine classifiers. The results indicate that the method reaches an accuracy of 92%, which is obtained by the plurality voting method using data subset prediction classes. Comparing the results shows that the final result is more accurate than the results of each individual data subset, and the heterogeneous voting method is advantageous when balancing out individual weaknesses of a set of equally well-performing models.

Cite this article as:
L. Wang, Z. Huang, S. Hao, Y. Cheng, and Y. Yang, “A Heterogeneous Ensemble Learning Voting Method for Fatigue Detection in Daily Activities,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.1, pp. 88-96, 2018.
Data files:
  1. [1] L. Sanchez-Medina and J. J. González-Badillo, “Velocity Loss as an Indicator of Neuromuscular Fatigue During Resistance Training,” Medicine & Science in Sports & Exercise, Vol.43, No.9, pp. 1725-1734, 2011.
  2. [2] C. D. Pollard, B. C. Heiderscheit, R. E. Van Emmerik, and J. Hamill, “Gender Differences in Lower Extremity Coupling Variability During an Unanticipated Cutting Maneuver,” J. of Applied Biomechanics, Vol.21, No.2, pp. 143-152, 2005.
  3. [3] S. M. Marcora, W. Staiano, and V. Manning, “Mental Fatigue Impairs Physical Performance in Humans,” J. of Applied Physiology, Vol.106, No.3, pp. 857-864, 2009.
  4. [4] N. Cortes, J. Onate, and S. Morrison, “Differential Effects of Fatigue on Movement Variability,” Gait & Posture, Vol.39, No.3, pp. 888-893, 2014.
  5. [5] L. Jensen, M. Dancisak, and J. Korndorffer, “Muscle-Cooling Intervention to Reduce Fatigue and Fatigue-Induced Tremor in Novice and Experienced Surgeons: a Preliminary Investigation,” The Surgery Journal, Vol.2, No.4, pp. e126-e130, 2016.
  6. [6] C. Prakash, R. Kumar, and N. Mittal, “Recent Developments in Human Gait Research: Parameters, Approaches, Applications, Machine Learning Techniques, Datasets and Challenges,” Artificial Intelligence Review, pp. 1-40, 2016.
  7. [7] C. Prakash , R. Kumar, and N. Mittal, “Vision Based Gait Analysis Techniques in Elderly Life : a Survey,” Csi Communications, pp. 19-21, 2015.
  8. [8] A. Muro-De-La-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait Analysis Methods: an Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications,” Sensors, Vol.14, No.2, pp. 3362-3394, 2014.
  9. [9] J. Zhang, T. E. Lockhart, and R. Soangra, “Classifying Lower Extremity Muscle Fatigue During Walking Using Machine Learning and Inertial Sensors,” Annals of Biomedical Engineering, Vol.42, No.3, pp. 600-612, 2014.
  10. [10] B. T. Nukala, N. Shibuya, A. Rodriguez, J. Tsay, J. Lopez, T. Nguyen, S. Zupancic, and D. Y.-C. Lie, “An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms,” Open J. of Applied Biosensor, Vol.3, No.4, pp. 29-39, 2014.
  11. [11] Y. Song, J. Zhang, L. Cao, and M. Sangeux, “On Discovering the Correlated Relationship Between Static and Dynamic Data in Clinical Gait Analysis,” Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, pp. 563-578, 2013.
  12. [12] S. Raschka, “Python Machine Learning,” Packt Publishing Ltd, Birmingham, UK, 2015.
  13. [13] R. Prashanth, S. D. Roy, P. K. Mandal, and S. Ghosh, “High-Accuracy Detection of Early Parkinson’s Disease through Multimodal Features and Machine Learning,” Int. J. of Medical Informatics, Vol.90, pp. 13-21, 2016.
  14. [14] K. Vinay, A. Rao, and G. H. Kumar, “Computerized Analysis of Classification of Lung Nodules and Comparison between Homogeneous and Heterogeneous Ensemble of Classifier Model,” 2011 Third National Conf. on Computer Vision, Pattern Recognition, Image Proc. and Graphics (NCVPRIPG), pp. 231-234, 2011.
  15. [15] Z. H. Zhou, “Ensemble Methods: Foundations and Algorithms,” Chapman & Hall/Crc Machine Learnig & Pattern Recognition, CRC press, 2012.
  16. [16] V. S. Sheng, F. Provost, and P. G. Ipeirotis, “Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers,” Proc. of the 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 614-622, 2008.
  17. [17] T. G. Dietterich, “Ensemble Methods in Machine Learning,” Int. Workshop on Multiple Classifier Systems, Vol.1857, pp. 1-15, 2000.
  18. [18] T. G. Dietterich, “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization,” Machine Learning, Vol.40, No.2, pp. 139-157, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 23, 2024