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Post-fall Detection Using ANN Based on Ranking Algorithms

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Abstract

The purpose of this study was to develop an accurate and efficient algorithm for post-fall detection. Thirty healthy male subjects were recruited and asked to perform 23 movements, comprising 14 activities of daily living and nine fall motions. The algorithm was developed using the ANN toolbox provided with MATLAB and inertial measurement unit (IMU) data were used to distinguish between fall and non-fall cases. An IMU sensor was located at the center between the left and right anterior superior iliac spines. A total of 32 feature vectors were extracted from 3-axis acceleration and angular velocity signals. Based on the five different ranking algorithms (relief-F, T-score, correlation, Fisher score, and minimum redundancy maximum relevance) used, feature vector subsets comprising the feature vectors were created and subsequently evaluated. Accuracy was compared according to the number of feature vectors constituting the subset, which were based on rank-lists. The results showed that the subset comprising all the feature vectors showed the best accuracy (99.86%), but a similar accuracy could be obtained with a subset comprising fewer feature vectors. The T-score was found to be the most optimal among the five ranking algorithms. Furthermore, T-score with two feature vectors achieved an accuracy of 99.17%. The results of this study are expected to assist in the construction of subsets of feature vectors based on ranking algorithms for post-fall detection with high accuracy and less computational cost.

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References

  1. World Health Organization. (2011). World Health Organization global report on falls prevention in older age, 2007.

  2. Baldewijns, G., Debard, G., Mertes, G., Croonenborghs, T., & Vanrumste, B. (2017). Improving the accuracy of existing camera based fall detection algorithms through late fusion. In 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 2667–2671). IEEE.

  3. Fleming, J., & Brayne, C. (2008). Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90. BMJ, 337, a2227.

    Article  Google Scholar 

  4. King, M. B., & Tinetti, M. E. (1995). Falls in community-dwelling older persons. Journal of the American Geriatrics Society, 43(10), 1146–1154.

    Article  Google Scholar 

  5. Ahn, S., Choi, D., Kim, J., Kim, S., Jeong, Y., Jo, M., et al. (2018). Optimization of a pre-impact fall detection algorithm and development of hip protection airbag system. Sensors and Materials, 30, 1743–1752.

    Article  Google Scholar 

  6. Yao, M., Zhang, Q., Li, M., Li, H., Ning, Y., Xie, G., & Jin, Z. (2015). A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication. In 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN) (pp. 1–6). IEEE.

  7. Yi, W. J., Wang, B., dos Santos, B. F., Carvalho, E. F., & Saniie, J. (2018). Design flow of neural network application for IoT based fall detection system. In 2018 IEEE international conference on electro/information technology (EIT) (pp. 0578–0582). IEEE.

  8. Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2019). Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing, 23(5–6), 801–817.

    Article  Google Scholar 

  9. Chelli, A., & Pätzold, M. (2019). A machine learning approach for fall detection and daily living activity recognition. IEEE Access, 7, 38670–38687.

    Article  Google Scholar 

  10. Vallabh, P., Malekian, R., Ye, N., & Bogatinoska, D. C. (2016). Fall detection using machine learning algorithms. In 2016 24th international conference on software, telecommunications and computer networks (SoftCOM) (pp. 1–9). IEEE.

  11. Özdemir, A. T., & Barshan, B. (2014). Detecting falls with wearable sensors using machine learning techniques. Sensors, 14(6), 10691–10708.

    Article  Google Scholar 

  12. Yoo, S., & Oh, D. (2018). An artificial neural network–based fall detection. International Journal of Engineering Business Management, 10, 1847979018787905.

    Article  Google Scholar 

  13. Nukala, B. T., Shibuya, N., Rodriguez, A., Tsay, J., Lopez, J., Nguyen, T., et al. (2015). An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms. Open Journal of Applied Biosensor, 3(04), 29.

    Article  Google Scholar 

  14. Kim, S., Kim, J., Koo, B., Kim, T., Jung, H., Park, S., et al. (2019). Development of an armband EMG module and a pattern recognition algorithm for the 5-finger myoelectric hand prosthesis. International Journal of Precision Engineering and Manufacturing, 20(11), 1997–2006.

    Article  Google Scholar 

  15. Vasan, K. K., & Surendiran, B. (2017). Feature subset selection for intrusion detection using various rank-based algorithms. International Journal of Computer Applications in Technology, 55(4), 298–307.

    Article  Google Scholar 

  16. Ojetola, O., Gaura, E., & Brusey, J. (2015). Data set for fall events and daily activities from inertial sensors. In Proceedings of the 6th ACM multimedia systems conference (pp. 243–248).

  17. Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E. G., & Tsiknakis, M. (2014). The mobifall dataset: Fall detection and classification with a smartphone. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2(1), 44–56.

    Article  Google Scholar 

  18. Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., & Tsiknakis, M. (2016). The MobiAct dataset: Recognition of activities of daily living using smartphones. In ICT4AgeingWell (pp. 143–151).

  19. Gasparrini, S., Cippitelli, E., Gambi, E., Spinsante, S., Wåhslén, J., Orhan, I., & Lindh, T. (2015). Proposal and experimental evaluation of fall detection solution based on wearable and depth data fusion. In International conference on ICT innovations (pp. 99–108). Cham: Springer.

  20. Vilarinho, T., Farshchian, B., Bajer, D. G., Dahl, O. H., Egge, I., Hegdal, S. S., & Weggersen, S. M. (2015). A combined smartphone and smartwatch fall detection system. In 2015 IEEE international conference on computer and information technology; Ubiquitous computing and communications; Dependable, autonomic and secure computing; Pervasive intelligence and computing (pp. 1443–1448). IEEE.

  21. Medrano, C., Igual, R., Plaza, I., & Castro, M. (2014). Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE. https://doi.org/10.1371/journal.pone.0094811.

    Article  Google Scholar 

  22. Sucerquia, A., López, J. D., & Vargas-Bonilla, J. F. (2017). SisFall: A fall and movement dataset. Sensors, 17(1), 198.

    Article  Google Scholar 

  23. Kwolek, B., & Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer Methods and Programs in Biomedicine, 117(3), 489–501.

    Article  Google Scholar 

  24. Micucci, D., Mobilio, M., & Napoletano, P. (2017). Unimib shar: A dataset for human activity recognition using acceleration data from smartphones. Applied Sciences, 7(10), 1101.

    Article  Google Scholar 

  25. Wertner, A., Czech, P., & Pammer-Schindler, V. (2015). An open labelled dataset for mobile phone sensing based fall detection. In Proceedings of the 12th EAI international conference on mobile and ubiquitous systems: Computing, networking and services on 12th EAI international conference on mobile and ubiquitous systems: Computing, networking and services (pp. 277–278).

  26. Frank, K., Vera Nadales, M. J., Robertson, P., & Pfeifer, T. (2010). Bayesian recognition of motion related activities with inertial sensors. In Proceedings of the 12th ACM international conference adjunct papers on ubiquitous computing-Adjunct (pp. 445–446).

  27. Putra, I. P. E. S., Brusey, J., Gaura, E., & Vesilo, R. (2018). An event-triggered machine learning approach for accelerometer-based fall detection. Sensors, 18(1), 20.

    Google Scholar 

  28. Khojasteh, S. B., Villar, J. R., Chira, C., González, V. M., & De la Cal, E. (2018). Improving fall detection using an on-wrist wearable accelerometer. Sensors, 18(5), 1350.

    Article  Google Scholar 

  29. Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249–256). Morgan Kaufmann Publishers, Inc.

  30. Kira, K., & Rendell, L. A. (1992). The feature selection problem: Traditional methods and a new algorithm. In AAAI (Vol. 2, pp. 129–134).

  31. Kononenko, I. (1994). Estimating attributes: analysis and extensions of RELIEF. In European conference on machine learning (pp. 171–182). Berlin, Heidelberg: Springer.

  32. Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., & Liu, H. (2010). Advancing feature selection research. In ASU feature selection repository (pp. 1–28).

  33. Hall, M. A. (2000). Correlation-based feature selection of discrete and numeric class machine learning. Working paper 00/08. Hamilton: University of Waikato, Department of Computer Science.

  34. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. Hoboken: Wiley.

    MATH  Google Scholar 

  35. Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.

    Article  Google Scholar 

  36. Hudgins, B., Parker, P., & Scott, R. N. (1993). A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40(1), 82–94.

    Article  Google Scholar 

  37. Du, Y. C., Lin, C. H., Shyu, L. Y., & Chen, T. (2010). Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Systems with Applications, 37(6), 4283–4291.

    Article  Google Scholar 

  38. Thelen, D. G., Wojcik, L. A., Schultz, A. B., Ashton-Miller, J. A., & Alexander, N. B. (1997). Age differences in using a rapid step to regain balance during a forward fall. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 52(1), M8–M13.

    Article  Google Scholar 

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Acknowledgement

This research was supported by Basic Science Research Program (No.2018R1D1A1B07048575) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and the Technology Innovation Program (No.20006386) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).

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  1. Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Republic of Korea

    Bummo Koo, Jongman Kim, Taehee Kim, Haneul Jung, Yejin Nam & Youngho Kim

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  1. Bummo Koo
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  2. Jongman Kim
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Correspondence to Youngho Kim.

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Koo, B., Kim, J., Kim, T. et al. Post-fall Detection Using ANN Based on Ranking Algorithms. Int. J. Precis. Eng. Manuf. 21, 1985–1995 (2020). https://doi.org/10.1007/s12541-020-00398-6

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  • DOI: https://doi.org/10.1007/s12541-020-00398-6

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