Abstract
Accurate estimation of biometric parameters recorded from subjects’ wrist or waist, when the subjects are performing various physical exercises, is often a challenging problem due to the presence of motion artifacts. In order to reduce the motion artifacts, data derived from a triaxial accelerometer have been proven to be very useful. Unfortunately, wearable devices such as smartphones and smartwatches are in general differently oriented during real life activities, so the data derived from the three axes are mixed up. This paper proposes an efficient technique for real-time recognition of human activities by using accelerometer data that is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition, that is independent of the orientation of the sensor. This is particularly suitable for implementation in wearable devices. In order to demonstrate the validity of this technique, it has been successfully applied to a database of accelerometer data derived from static postures, dynamic activities, and postural transitions occurring between the static postures.
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References
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphone-based activity recognition using fixed-point arithmetic. J. Univ. Comput. Sci. 19(9), 1295–1314 (2013)
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), pp. 437–442 (2013)
Bacà, A., Biagetti, G., Camilletti, M., Crippa, P., Falaschetti, L., Orcioni, S., Rossini, L., Tonelli, D., Turchetti, C.: CARMA: a robust motion artifact reduction algorithm for heart rate monitoring from PPG signals. In: 23rd European Signal Processing Conference (EUSIPCO 2015), pp. 2696–2700 (2015)
Biagetti, G., Crippa, P., Curzi, A., Orcioni, S., Turchetti, C.: Analysis of the EMG signal during cyclic movements using multicomponent AM-FM decomposition. IEEE J. Biomed. Health Inf. 19(5), 1672–1681 (2015)
Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: A rule based framework for smart training using sEMG signal. In: Intelligent Decision Technologies: Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), pp. 89–99. Sorrento, Italy (2015)
Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: Artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification. In: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pp. 467–474. Rome, Italy (2016)
Catal, C., Tufekci, S., Pirmit, E., Kocabag, G.: On the use of ensemble of classifiers for accelerometer-based activity recognition. Appl. Soft Comput. 37, 1018–1022 (2015)
Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: 8th International Conference on Intelligent Environments (IE), pp. 214–221 (2012)
Figueiredo, M.A.F., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381–396 (2002)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Khan, A., Lee, Y.K., Lee, S., Kim, T.S.: Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: 2010 5th International Conference on Future Information Technology (FutureTech), pp. 1–6 (2010)
Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)
Mannini, A., Intille, S.S., Rosenberger, M., Sabatini, A.M., Haskell, W.: Activity recognition using a single accelerometer placed at the wrist or ankle. Med. Sci. Sports Exerc. 45(11), 2193–2203 (2013)
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. Speech Audio Process. 3(1), 72–83 (1995)
Rodriguez-Martin, D.: Samà, A., Perez-Lopez, C., Català, A., Cabestany, J., Rodriguez-Molinero, A.: SVM-based posture identification with a single waist-located triaxial accelerometer. Expert Syst. Appl. 40(18), 7203–7211 (2013)
Torres-Huitzil, C., Nuno-Maganda, M.: Robust smartphone-based human activity recognition using a tri-axial accelerometer. In: 2015 IEEE 6th Latin American Symposium on Circuits Systems (LASCAS), pp. 1–4 (2015)
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Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C. (2016). An Efficient Technique for Real-Time Human Activity Classification Using Accelerometer Data. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_36
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DOI: https://doi.org/10.1007/978-3-319-39630-9_36
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