Abstract
Falls are the leading cause of death and non-fatal injuries among older people, thus pre-impact fall detection that detects a fall before body-ground impact is of crucial significance. 32 young subjects performed different types of falls and daily activities, and their motion data was recorded by a wearable inertial sensor to establish a large-scale motion dataset. Five commonly used machine learning algorithms were applied and compared thoroughly in terms of accuracy and practicality for pre-impact fall detection. Results showed that in terms of sensitivity, specificity and lead time, both support vector machine (SVM: 99.77%, 93.10%, 362 ± 150 ms) and random forest (RF: 100%, 92.90%, 357 ± 145 ms) achieved better results than other 3 models. SVM showed a much shorter latency (66 ms) than RF (1047 ms) running in a microcontroller. Those findings suggest that SVM has the highest potential to be embedded into a wearable sensor based system to provide real-time fall protection for the elderly.
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References
Lord, S.R., et al.: Falls in Older People: Risk Factors and Strategies for Prevention, 2nd edn. Cambridge University Press, Cambridge (2007)
Bergen, G., Stevens, M.R., Burns, E.R.: Falls and fall injuries among adults aged≥ 65 years—United States, 2014. Morb. Mortal. Wkly Rep. 65(37), 993–998 (2016)
Rehm, J., et al.: Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. The Lancet. 373(9682), 2223–2233 (2009)
Vallabh, P., Malekian, R.: Fall detection monitoring systems: a comprehensive review. J. Ambient. Intell. Humaniz. Comput. 9(6), 1809–1833 (2017). https://doi.org/10.1007/s12652-017-0592-3
Hu, X., Qu, X.: Pre-impact fall detection. Biomed. Eng. Online 15(1), 61 (2016)
Bourke, A.K., O'Donovan, K.J., Ólaighin, G.: The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls. Med. Eng. Phys. 30(7), 937–946 (2008)
Sabatini, A.M., et al.: Prior-to-and post-impact fall detection using inertial and barometric altimeter measurements. IEEE Trans. Neural Syst. Rehabil. Eng. 24(7), 774–783 (2015)
Shan, S., Yuan, T.: A wearable pre-impact fall detector using feature selection and support vector machine. In: IEEE 10th International Conference On Signal Processing Proceedings. IEEE (2010)
Aziz, O., et al: The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (2014)
Sucerquia, A., López, J.D., Vargas-Bonilla, J.F.: SisFall: A fall and movement dataset. Sensors 17(1), 198 (2017)
Casilari, E., Santoyo-Ramón, J.-A., Cano-García, J.-M.: Analysis of public datasets for wearable fall detection systems. Sensors 17(7), 1513 (2017)
Musci, M., et al.: Online fall detection using recurrent neural networks on smart wearable devices. IEEE Trans. Emerg. Top. Comput. 1 (2020). https://doi.org/10.1109/TETC.2020.3027454
Incel, O.D.: Analysis of movement, orientation and rotation-based sensing for phone placement recognition. Sensors 15(10), 25474–25506 (2015)
Lee, J, Kim, J.: Energy-efficient real-time human activity recognition on smart mobile devices. Mobile Inf. Syst. 2016, 1–12 (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media (2009)
Kim, T.H., et al.: Machine learning-based pre-impact fall detection model to discriminate various types of fall. J. Biomech. Eng. 141(8) (2019)
Qiu, H., et al.: Application of wearable inertial sensors and a new test battery for distinguishing retrospective fallers from non-fallers among community-dwelling older people. Sci. Rep. 8, 16349 (2018)
Tao, X., Yun, Z.: Fall prediction based on biomechanics equilibrium using Kinect. Int. J. Distrib. Sens. Netw. 13(4), 1550147717703257 (2017)
Acknowledgments
This work was funded by the KAIST End-Run Project and partially supported by the Basic Science Research Program of the National Research Foundation of Korea [NRF2017R1C1B2006811] and the China Scholarship Council.
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Yu, X., Jang, J., Xiong, S. (2021). Machine Learning-Based Pre-impact Fall Detection and Injury Prevention for the Elderly with Wearable Inertial Sensors. In: Goonetilleke, R.S., Xiong, S., Kalkis, H., Roja, Z., Karwowski, W., Murata, A. (eds) Advances in Physical, Social & Occupational Ergonomics. AHFE 2021. Lecture Notes in Networks and Systems, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-80713-9_36
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