Determining the optimal number of body-worn sensors for human activity recognition
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Recent developments in sensors increased the importance of action recognition. Generally, the previous studies were based on the assumption that the complex actions can be recognized by more features. Therefore, generally more than required body-worn sensor types and sensor nodes were used by the researchers. On the other hand, this assumption leads many drawbacks, such as computational complexity, storage and communication requirements. The main aim of this paper is to investigate the applicability of recognizing the actions without degrading the accuracy with less number of sensors by using a more sophisticated feature extraction and classification method. Since, human activities are complex and include variable temporal information in nature, in this study one-dimensional local binary pattern, which is sensitive to local changes, and the grey relational analysis, which can successfully classify incomplete or insufficient datasets, were employed for feature extraction and classification purposes, respectively. Achieved mean classification accuracies by the proposed approach are 95.69, 98.88, and 99.08 % while utilizing all data, data obtained from a sensor node attached to left calf and data obtained from only 3D gyro sensors, respectively. Furthermore, the results of this study showed that the accuracy obtained by using only a 3D acceleration sensor attached in the left calf, 98.8 %, is higher than accuracy obtained by using all sensor nodes, 95.69 %, and reported accuracies in the previous studies that made use of the same dataset. This result highlighted that the position and type of sensors are much more important than the number of utilized sensors.
KeywordsSensor reduction Activity recognition Local binary patterns Grey relational analysis Wearable sensors
Compliance with ethical standards
Conflicts of interest
Author Ömer Faruk Ertugrul declares that he has no conflict of interest. Author Y\(\imath \)lmaz Kaya declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bache K, Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. http://archive.ics.uci.edu/ml
- Banos O, Damas M, Pomares H, Rojas I (2013) Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the international work conference on neural networks (IWANN 2013), TenerifeGoogle Scholar
- Banos O, Damas M, Pomares H, Rojas I (2013) Handling displacement effects in on-body sensor-based activity recognition. In: Ambient assisted living and active aging, pp 80–87. doi: 10.1007/978-3-319-03092-0_12
- Banos O, Toth MA, Damas M, Pomares H, Rojas I, Amft O (2012) A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the 14th international conference on ubiquitous computing (Ubicomp 2012), PittsburghGoogle Scholar
- Hachaj T, Ogiela MR, Koptyra K (2015) Application of assistive computer vision methods to oyama karate techniques recognition. Symmetry 7(4):1670–1698. doi: 10.1016/j.dsp.2015.07.004
- Hachaj T, Ogiela MR (2014) Rule-based approach to recognizing human body poses and gestures in real time. Multimed Syst 20(1):81–99. doi: 10.1007/s00530-013-0332-2
- Hachaj T, Ogiela MR (2015) Full body movements recognition–unsupervised learning approach with heuristic R-GDL method. Dig Signal Process 46:239–252. doi: 10.1016/j.dsp.2015.07.004
- Kaya Y, Uyar M, Tekin R, Yıldırım S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209–219. doi: 10.1016/j.amc.2014.05.128
- Ladha L, Deepa T (2011) Feature selection methods and algorithms. Int J Comput Sci Eng (IJCSE) 3(5):1787–1797Google Scholar
- Lin Y, Liu S (2004) A historical introduction to grey systems theory. Proc IEEE Int Conf Syst Man Cybern 1:2403–2408Google Scholar
- Punchoojit L, Hongwarittorrn N (2015) A comparative study on sensor displacement effect on realistic sensor displacement benchmark dataset. In: Recent advances in information and communication technology, pp 97–106. doi: 10.1007/978-3-319-19024-2_10
- Renyi A (1961) On measures of entropy and information. In: Fourth Berkeley symposium on mathematical statistics and probability, pp 547–561Google Scholar
- Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL (2012) Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications. Maturitas 71:13–19. doi: 10.1016/j.maturitas.2011.11.003 CrossRefGoogle Scholar
- Wilson J, Najjar N, Hare J, Gupta S (2015) Human activity recognition using LZW-coded probabilistic finite state automata. In: IEEE international conference on robotics and automation (ICRA), pp 3018–3023. doi: 10.1109/ICRA.2015.7139613
- Ye J, Stevenson G, Dobson S (2014) KCAR: a knowledge-driven approach for concurrent activity recognition. Pervasive Mobile Comput. doi: 10.1016/j.pmcj.2014.02.003 (in Press)