Human Activity Recognition by Matching Curve Shapes
In this paper, we present a new method for Human Activity Recognition (HAR) from body-worn accelerometers or inertial sensors using comparison of curve shapes.
Simple motion activities have characteristic patterns that are visible in the time series representations of the sensor data. These time series representations, such as the 3D accelerations or the Euler angles (roll, pitch and yaw), can be treated as curves and activities can be recognized by matching patterns (shapes) in the curves using curve comparison and alignment techniques.
We transform the sensor signals into cubic B-splines and parametrize the curves with respect to arc length for comparison. We tested our algorithm on the accelerometer data collected at Cleveland State University . The 3D acceleration signals were segmented at high-level and subject-dependent ‘representative’ curves for the activities were constructed with which test curves were compared and labeled with an overall accuracy rate of 88.46% by our algorithm.
KeywordsActivity Recognition Cubic B-splines Arc-Length Para-metrization Curve Comparison
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- 1.Sanka, S., Reddy, P.G., Alt, A., Reinthal, A., Sridhar, N.: Utilization of a wrist-mounted accelerometer to count movement repetitions. In: Fourth International Conference on Communication Systems and Networks, pp. 1–6. IEEE (2012)Google Scholar
- 3.Ramsay, J., Silverman, B., Hooker, G., Graves, S.: Functional Data Analysis Home Page, http://www.psych.mcgill.ca/misc/fda/
- 5.Wang, H., Kearney, J., Atkinson, K.: Arc-length parameterized spline curves for real-time simulation. In: Fifth International Conference on Curves and Surfaces, pp. 387–396 (2002)Google Scholar
- 6.Femiani, J.C., Razdan, A., Farin, G.: Curve Shapes: Comparison and AlignmentGoogle Scholar
- 7.Faires, J., Burden, R.: Numerical Methods, 3rd edn. Brooks Cole (2002)Google Scholar
- 8.Bulling, A., Blanke, U., Schiele, B.: A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Computing Surveys 46(3), 33:1–33:33 (2014)Google Scholar
- 9.Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: International Conference on Data Mining, pp. 289–296. IEEE (2001)Google Scholar
- 10.Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Sagha, H., Bayati, H., Creatura, M., del, R., Millan, J.: Collecting complex activity data sets in highly rich networked sensor environments. In: Seventh International Conference on Networked Sensing Systems, pp. 233–240. IEEE (2010)Google Scholar
- 11.OPPORTUNITY Activity Recognition Dataset Home Page, https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition