Abstract
In this paper, we propose an algorithmic approach for a motion analysis framework to automatically recognize local muscular endurance (LME) exercises and to count their repetitions using a wrist-worn inertial sensor. LME exercises are prescribed for cardiovascular disease rehabilitation. As a technical solution, we propose activity recognition based on machine learning. We developed an algorithm to automatically segment the captured data from all participants. Relevant time and frequency domain features were extracted using a sliding window technique. Principal component analysis (PCA) was applied for dimensionality reduction of the extracted features. We trained 15 binary classifiers using support vector machine (SVM) to recognize individual LME exercises, achieving overall accuracy of more than 98%. We applied grid search technique to obtain the optimal SVM hyperplane parameters. The learning curves (mean ± stdev) for each model is investigated to verify that the models were not over-fitted and performed well on any new test data. Also, we devised a method to count the repetitions of the upper body exercises.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davies, P., Taylor, F., Beswick, A., Wise, F., Moxham, T., Rees, K., Ebrahim, S.: Promoting patient uptake and adherence in cardiac rehabilitation. Cochrane Database Syst. Rev. 7(7) (2010)
Mitchell, E., Ahmadi, A., O’Connor, N.E., Richter, C., Farrell, E., Kavanagh, J., Moran, K.: Automatically detecting asymmetric running using time and frequency domain features. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–6. IEEE (2015)
Ahmadi, A., Destelle, F., Unzueta, L., Monaghan, D.S., Linaza, M.T., Moran, K., O’Connor, N.E.: 3D human gait reconstruction and monitoring using body-worn inertial sensors and kinematic modeling. IEEE Sens. J. 16(24), 8823–8831 (2016)
Ahmadi, A., Mitchell, E., Richter, C., Destelle, F., Gowing, M., O’Connor, N.E., Moran, K.: Toward automatic activity classification and movement assessment during a sports training session. IEEE Internet of Things J. 2(1), 23–32 (2015)
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. (CSUR) 46(3), 33 (2014)
Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17 (2004)
Mortazavi, B.J., Pourhomayoun, M., Alsheikh, G., Alshurafa, N., Lee, S.I., Sarrafzadeh, M.: Determining the single best axis for exercise repetition recognition and counting on smartwatches. In: 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 33–38. IEEE (2014)
Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15(12), 31314–31338 (2015)
Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International Conference on Body Area Networks, pp. 92–98. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2011)
Smith, L.I.: A tutorial on principal components analysis. Cornell Univ. USA 51(52), 65 (2002)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D.: A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56(3), 871–879 (2009)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2001)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
Acknowledgement
The work was funded by ACQUIS BI, an industrial partner of INSIGHT Centre for Data Analytics, DCU and Science Foundation Ireland under Grant Number SFI/12/RC/2289.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Prabhu, G., Ahmadi, A., O’Connor, N.E., Moran, K. (2018). Activity Recognition of Local Muscular Endurance (LME) Exercises Using an Inertial Sensor. In: Lames, M., Saupe, D., Wiemeyer, J. (eds) Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017). IACSS 2017. Advances in Intelligent Systems and Computing, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-67846-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-67846-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67845-0
Online ISBN: 978-3-319-67846-7
eBook Packages: EngineeringEngineering (R0)