Öz
In this study, the methods which can detect the basic physical movements of a person (downward, upward, sitting, stop, walking, running) from inertial sensor (IMU) data are evaluated. The performances of classical (ANN, SVM, k-NN) and current approaches (Convolutional Neural Networks-ESA) to map IMU data to activity classes were compared. A three-stage study was carried out for this aim: 1) data acquisition; 2) creating training/test sets; 3) construction and classification of network architectures. At the stage of data acquisition, to obtain 6 different physical movements from 10 different people, the accelerometer sensor is placed on the persons. Repetitive movements of persons were recorded. At the second stage, the recorded long-term accelerometer data is divided into packages in the form of short-term windows. The training set of classical approaches was constructed by features extracting from each packet data containing one-dimensional acceleration information. The transformation of one-dimensional signals to a two-dimensional image matrix for the training set of the deep learning-based approaches was performed. In the third stage, ANN, SVM, k-NN and CNN architectures were constructed, and classification process was carried out. As a result of the experimental studies, it was found that the accuracy of IMU-activity mapping was 99% with the ANN method and 95% with the CNN method.
Anahtar Kelimeler
Kaynakça
- [1] N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell, “A survey of mobile phone sensing,” IEEE Commun. Mag., 2010.
- [2] W. Zijlstra and K. Aminian, “Mobility assessment in older people: New possibilities and challenges,” European Journal of Ageing. 2007.
- [3] D. Roetenberg, P. J. Slycke, and P. H. Veltink, “Ambulatory position and orientation tracking fusing magnetic and inertial sensing,” IEEE Trans. Biomed. Eng., 2007.
- [4] P. Prasertsung and T. Horanont, “A classification of accelerometer data to differentiate pedestrian state,” in 20th International Computer Science and Engineering Conference: Smart Ubiquitos Computing and Knowledge, ICSEC 2016, 2017.
- [5] X. Su, H. Tong, and P. Ji, “Activity recognition with smartphone sensors,” Tsinghua Sci. Technol., 2014.
- [6] U. Lindemann, A. Hock, M. Stuber, W. Keck, and C. Becker, “Evaluation of a fall detector based on accelerometers: A pilot study,” Med. Biol. Eng. Comput., 2005.
- [7] E. Jovanov, A. Milenkovic, C. Otto, and P. C. De Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” J. Neuroeng. Rehabil., 2005.
- [8] E. A. Sağbaş and S. Balli, “Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti Transportation mode detection by using smartphone sensors and machine learning,” Pamukkale Univ Muh Bilim Derg, 2016.
- [9] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SIGKDD Explor. Newsl., 2011.
- [10] M. Shoaib, S. Bosch, H. Scholten, P. J. M. Havinga, and O. D. Incel, “Towards detection of bad habits by fusing smartphone and smartwatch sensors,” in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015, 2015.
- [11] D. Natarajasivan and M. Govindarajan, “Filter Based Sensor Fusion for Activity Recognition using Smartphone,” Int. J. Comput. Sci. Telecommun. J. Homepage, 2016.
- [12] F. Dadashi et al., “A hidden Markov model of the breaststroke swimming temporal phases using wearable inertial measurement units,” in 2013 IEEE International Conference on Body Sensor Networks, BSN 2013, 2013.
- [13] B. J. Mortazavi, M. Pourhomayoun, G. Alsheikh, N. Alshurafa, S. I. Lee, and M. Sarrafzadeh, “Determining the single best axis for exercise repetition recognition and counting on smartwatches,” in Proceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014, 2014.
- [14] J. J. Guiry, P. van de Ven, and J. Nelson, “Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices,” Sensors (Switzerland), 2014.
- [15] G. M. Weiss, J. L. Timko, C. M. Gallagher, K. Yoneda, and A. J. Schreiber, “Smartwatch-based activity recognition: A machine learning approach,” in 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, 2016.
- [16] “MTw Avinda Software.” [Online]. Available: https://www.xsens.com/mt-software-suite-mtw-awinda/. [Accessed: 10-Dec-2018].
- [17] X. Xin, C. Wang, X. Ying, and B. Wang, “Deep community detection in topologically incomplete networks,” Phys. A Stat. Mech. its Appl., 2017.
- [18] S. E. Buttrey and C. Karo, “Using k-nearest-neighbor classification in the leaves of a tree,” Comput. Stat. Data Anal., 2002.
- [19] N. Hajibandeh, F. Faghihi, H. Ranjbar, and H. Kazari, “Classifications of disturbances using wavelet transform and support vector machine,” Turkish J. Electr. Eng. Comput. Sci., 2017.
- [20] S. M. S. Shah, S. Batool, I. Khan, M. U. Ashraf, S. H. Abbas, and S. A. Hussain, “Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis,” Phys. A Stat. Mech. its Appl., 2017.
- [21] R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Syst. Appl., 2013.
- [22] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic gradient descent,” ICLR Int. Conf. Learn. Represent., 2015.
Öz
In this study, the
methods which can detect the basic physical movements of a person (downward,
upward, sitting, stop, walking, running) from inertial sensor data are evaluated.
The performances of classical and current approaches to map IMU data to
activity classes were compared. A three-stage study was carried out for this
aim: 1) data acquisition; 2) creating training/test sets; 3) construction and
classification of network architectures. At the stage of data acquisition, to
obtain 6 different physical movements from 10 different people, the
accelerometer sensor is placed on the persons. Repetitive movements of persons
were recorded. At the second stage, the recorded long-term accelerometer data
is divided into packages in the form of short-term windows. The training set of
classical approaches was constructed by features extracting from each packet
data containing one-dimensional acceleration information. The transformation of
one-dimensional signals to a two-dimensional image matrix for the training set
of the deep learning-based approaches was performed. In the third stage, ANN,
SVM, k-NN and CNN architectures were constructed, and classification process
was carried out. As a result of the experimental studies, it was found that the
accuracy of IMU-activity mapping was 99% with the ANN method and 95% with the
CNN method.
Anahtar Kelimeler
Kaynakça
- [1] N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell, “A survey of mobile phone sensing,” IEEE Commun. Mag., 2010.
- [2] W. Zijlstra and K. Aminian, “Mobility assessment in older people: New possibilities and challenges,” European Journal of Ageing. 2007.
- [3] D. Roetenberg, P. J. Slycke, and P. H. Veltink, “Ambulatory position and orientation tracking fusing magnetic and inertial sensing,” IEEE Trans. Biomed. Eng., 2007.
- [4] P. Prasertsung and T. Horanont, “A classification of accelerometer data to differentiate pedestrian state,” in 20th International Computer Science and Engineering Conference: Smart Ubiquitos Computing and Knowledge, ICSEC 2016, 2017.
- [5] X. Su, H. Tong, and P. Ji, “Activity recognition with smartphone sensors,” Tsinghua Sci. Technol., 2014.
- [6] U. Lindemann, A. Hock, M. Stuber, W. Keck, and C. Becker, “Evaluation of a fall detector based on accelerometers: A pilot study,” Med. Biol. Eng. Comput., 2005.
- [7] E. Jovanov, A. Milenkovic, C. Otto, and P. C. De Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” J. Neuroeng. Rehabil., 2005.
- [8] E. A. Sağbaş and S. Balli, “Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti Transportation mode detection by using smartphone sensors and machine learning,” Pamukkale Univ Muh Bilim Derg, 2016.
- [9] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SIGKDD Explor. Newsl., 2011.
- [10] M. Shoaib, S. Bosch, H. Scholten, P. J. M. Havinga, and O. D. Incel, “Towards detection of bad habits by fusing smartphone and smartwatch sensors,” in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015, 2015.
- [11] D. Natarajasivan and M. Govindarajan, “Filter Based Sensor Fusion for Activity Recognition using Smartphone,” Int. J. Comput. Sci. Telecommun. J. Homepage, 2016.
- [12] F. Dadashi et al., “A hidden Markov model of the breaststroke swimming temporal phases using wearable inertial measurement units,” in 2013 IEEE International Conference on Body Sensor Networks, BSN 2013, 2013.
- [13] B. J. Mortazavi, M. Pourhomayoun, G. Alsheikh, N. Alshurafa, S. I. Lee, and M. Sarrafzadeh, “Determining the single best axis for exercise repetition recognition and counting on smartwatches,” in Proceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014, 2014.
- [14] J. J. Guiry, P. van de Ven, and J. Nelson, “Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices,” Sensors (Switzerland), 2014.
- [15] G. M. Weiss, J. L. Timko, C. M. Gallagher, K. Yoneda, and A. J. Schreiber, “Smartwatch-based activity recognition: A machine learning approach,” in 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, 2016.
- [16] “MTw Avinda Software.” [Online]. Available: https://www.xsens.com/mt-software-suite-mtw-awinda/. [Accessed: 10-Dec-2018].
- [17] X. Xin, C. Wang, X. Ying, and B. Wang, “Deep community detection in topologically incomplete networks,” Phys. A Stat. Mech. its Appl., 2017.
- [18] S. E. Buttrey and C. Karo, “Using k-nearest-neighbor classification in the leaves of a tree,” Comput. Stat. Data Anal., 2002.
- [19] N. Hajibandeh, F. Faghihi, H. Ranjbar, and H. Kazari, “Classifications of disturbances using wavelet transform and support vector machine,” Turkish J. Electr. Eng. Comput. Sci., 2017.
- [20] S. M. S. Shah, S. Batool, I. Khan, M. U. Ashraf, S. H. Abbas, and S. A. Hussain, “Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis,” Phys. A Stat. Mech. its Appl., 2017.
- [21] R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Syst. Appl., 2013.
- [22] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic gradient descent,” ICLR Int. Conf. Learn. Represent., 2015.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Mart 2021 |
| Gönderilme Tarihi | 11 Ekim 2019 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 24 Sayı: 1 |
Kaynak Göster
Cited By
Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.35234/fumbd.1323422
Bu eser Creative Commons Atıf-AynıLisanslaPaylaş 4.0 Uluslararası ile lisanslanmıştır.