Machine learning algorithms for fall detection using kinematic and heart rate parameters – a comprehensive analysis

Anita Ramachandran, Adarsh Ramesh, Aditya Sukhlecha, Avtansh Pandey, Anupama Karuppiah

Abstract


The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection (i) using a combination of features from 2 sensors – an IMU sensor and a heart rate sensor (ii) feature engineering and feature selection based on statistical methods and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device based fall detection systems.

Keywords


Ensemble techniques, Fall detection, Machine learning, Wearable devices

References


A. T. Özdemir, B. Barshan, Detecting falls with wearable sensors using machine learning techniques, Sensors (Basel), 2014, 14(6):10691-10708, doi: 10.3390/s140610691

S. Yu, H. Chen, R. A. Brown, Hidden Markov Model-based fall detection with motion sensor orientation calibration: A case for real-life home monitoring, IEEE Journal of Biomedical and Health Informatics, 2018, vol. 22, no. 6, pp. 1847-1853, doi: 10.1109/JBHI.2017.2782079

M. A. Guvensan, A. O. Kansiz, N. C. Camgoz, H. Turkmen, A. G. Yavuz, M. E. Karsligil, An energy-efficient multi-tier architecture for fall detection on smartphones, Sensors (Basel), 2017, 17(7):1487, doi: 10.3390/s17071487

M. V. Albert, K. Kording, M. Herrmann, A. Jayaraman, Fall classification by machine learning using mobile phones, PLoS One 2012, 7: e36556, doi: 10.1371/journal.pone.0036556

Y. Choi, A. S. Ralhan, S. Ko, A study on machine learning algorithms for fall detection and movement classification, International Conference on Information Science and Applications, 2011, pp. 1-8, 2011, doi: 10.1109/ICISA.2011.5772404

A. Jefiza, E. Pramunanto, H. Boedinoegroho, M. H. Purnomo, Fall detection based on accelerometer and gyroscope using back propagation, 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 1-6, doi: 10.1109/EECSI.2017.8239149

F. Hossain, M. L. Ali, M. Z. Islam, H. Mustafa, A direction-sensitive fall detection system using single 3D accelerometer and learning classifier, International Conference on Medical Engineering, Health Informatics and Technology (MediTec), 2016, pp. 1-6, doi: 10.1109/MEDITEC.2016.7835372

P. Vallabh, R. Malekian, N. Ye, D. C. Bogatinoska, Fall detection using machine learning algorithms, 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2016, pp. 1-9, doi: 10.1109/SOFTCOM.2016.7772142

X. Yang, A. Dinh, A, L. Che, A wearable real-time fall detector based on Naive Bayes Classifier, Proceedings of the 23rd Canadian Conference on Electrical and Computer Engineering (CCECE), 2010, pp. 1–4

S. Zhao, W. Li, W. Niu, R. Gravina, G. Fortino, Recognition of human fall events based on single tri-axial gyroscope, IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 2018, pp. 1-6, doi: 10.1109/ICNSC.2018.8361365

J. He, S. Bai, X. Wang, An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier, Sensors, 2017;17 (6): 1393, 2017, doi:10.3390/s17061393

A. Chelli and M. Pätzold, A machine learning approach for fall detection and daily living activity recognition, IEEE Access, vol. 7, pp. 38670-38687, 2019, doi: 10.1109/ACCESS.2019.2906693

H. Wang, M. Li, J. Li, J. Cao, Z. Wang, An improved fall detection approach for elderly people based on feature weight and Bayesian classification, IEEE International Conference on Mechatronics and Automation, 2016, pp. 471-476, doi: 10.1109/ICMA.2016.7558609

P. Tsinganos and A. Skodras, A smartphone-based fall detection system for the elderly, Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, Ljubljana, pp. 53-58, 2017, doi: 10.1109/ISPA.2017.8073568

H. Kao, J. Hung, C. Huang, GA-SVM applied to the fall detection system, 2017 International Conference on Applied System Innovation (ICASI), 2017, pp. 436-439, doi: 10.1109/ICASI.2017.7988446

A. Jahanjoo, M. N. Tahan, and M. J. Rashti, “Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm,” in Proceedings of the 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), pp. 90–95, Shahrekord, Iran, April 2017

D. Genoud, V. Cuendet, J. Torrent, Soft fall detection using machine learning in wearable devices, IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016, pp. 501-505, doi: 10.1109/AINA.2016.124

G. Chetty, M. White, F. Akther, Smart phone based data mining for human activity recognition, Procedia Computer Science 46:1181-1187, 2015, 10.1016/j.procs.2015.01.031

G. Leoni, P. T. Endo, K. Monteiro, E. Rocha, I. Silva and T. G. Lynn, “Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks,” Sensors (Basel), 2019 19(7), pii: E1644, doi:10.3390/s19071644

M. Musci, D. Martini, N. Blago, T. Facchinetti and M. Piastra, “Online Fall Detection using Recurrent Neural Networks,” 2018, ArXiv, abs/1804.04976

A. Ramachandran, K. R. Anupama, A Survey on Recent Advances in Wearable Fall Detection Systems, BioMed Research International, vol 2020, doi: 10.1155/2020/2167160

A. Ramachandran, A. Ramesh, P. Pahwa, A. P. Atreyaa, S. Murari and K. R. Anupama, Performance Analysis of Machine Learning Algorithms for Fall Detection," 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Bogota, Colombia, 2019, pp. 1-6, doi: 10.1109/HealthCom46333.2019.9009442

A. Ramachandran, A. Ramesh and A. Karuppiah, "Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection," 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, 2020, pp. 110-114, doi: 10.1109/ICOIN48656.2020.9016479

B. Rodrigues, D. Salgado, M. Cordeiro, K. Osterwald, T. Freire, V. Lucena, E. Naves & N. Murray, “Fall Detection System by Machine Learning Framework for Public Health”, Procedia Computer Science 141. 358-365, doi: 10.1016/j.procs.2018.10.189

F. Luna-Perejón, MJ Domínguez-Morales, A. Civit-Balcells, “Wearable Fall Detector Using Recurrent Neural Networks”, Sensors (Basel) 2019 19(22):4885; doi:10.3390/s19224885

D. Giuffrida, G. Benetti, D. De Martini and T. Facchinetti, "Fall Detection with Supervised Machine Learning using Wearable Sensors," 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 2019, pp. 253-259, doi: 10.1109/INDIN41052.2019.8972246

A. Chelli and M. Pätzold, "A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition," in IEEE Access, vol. 7, pp. 38670-38687, 2019, doi: 10.1109/ACCESS.2019.2906693

N. Zurbuchen, P. Bruegger and A. Wilde, "A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors," 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 2020, pp. 427-431, doi: 10.1109/ICAIIC48513.2020.9065205

L. Rokach, “Ensemble-based classifiers”, Artificial Intelligence Review 33, 1–39 (2010), doi: 10.1007/s10462-009-9124-7




DOI: http://doi.org/10.11591/ijai.v9.i4.pp%25p
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