Abstract
As the elderly population grows, fall prediction and prevention becomes a crucial subject for investigation. This work explores a novel approach using unobtrusive sensor such as pressure mats and machine learning (ML) algorithms to continuously monitor gait patterns and predict fall risks in older adults. Sensing pressure mat was denoised and used to acquire movement datasets from an individual ranging from normal walking to performance of various activities such as falls, jumps and the application of uneven foot pressure while walking or exercising in a lab environment that mimics a home environment. Data gleaned from the sensor were cleaned, visualised, analysed and used to design a fall prediction model using a decision tree algorithm. Experimental results indicated a balanced performance (overall accuracy of 80% and F1 score of 88.64%). This paves the way for innovative fall prediction and prevention strategies using sensor mats and ML, ultimately benefiting the wellbeing of the aging population. Future research will utilise real-world data from unobtrusive sensing solutions such as pressure mats and more advanced ML algorithms to model a fall prediction system.
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References
Office for National Statistics. Living longer - how our population is changing and why it matters. Office for National Statistics (2018)
‘Falls’, NHS. https://www.nhs.uk/conditions/falls/. Accessed 15 Jun 2024
Mortality from accidental falls: number, by age group, annual, MFP, NHS. https://digital.nhs.uk/data-and-information/publications/statistical/compendium-mortality/current/mortality-from-accidental-falls/mortality-from-accidental-falls-number-by-age-group-annual-mfp. Accessed 15 Jun 2024
Falls: applying All Our Health, GOV UK. https://www.gov.uk/government/publications/falls-applying-all-our-health/falls-applying-all-our-health. Accessed 15 Jun 2024
Dorri, S., Zabolinezhad, H., Sattari, M.: The application of Internet of Things for the elderly health safety: a systematic review (2023). https://doi.org/10.4103/abr.abr_197_22
Rafferty, J., et al.: Thermal vision based fall detection via logical and data driven processes. In: Proceedings - 2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019 (2019). https://doi.org/10.1109/BCD.2019.8884820
Gillain, S., et al.: Using supervised learning machine algorithm to identify future fallers based on gait patterns: a two-year longitudinal study. Exp. Gerontol. 127 (2019). https://doi.org/10.1016/j.exger.2019.110730
Begg, R., Kamruzzaman, J.: A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J. Biomech. 38(3) (2005). https://doi.org/10.1016/j.jbiomech.2004.05.002
Salzman, B.: Gait and balance disorders in older adults (2011)
Tideiksaar, R.: Falls and instability in the elderly. NeuroRehabilitation 3(1) (1993). https://doi.org/10.3233/NRE-1993-3108
Atoyebi, O.A., Stewart, A., Sampson, J.: Use of information technology for falls detection and prevention in the elderly (2015). https://doi.org/10.1007/s12126-014-9204-0
Espinosa, R., Ponce, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., Moya-Albor, E.: A vision-based approach for fall detection using multiple cameras and convolutional neural networks: a case study using the UP-Fall detection dataset. Comput. Biol. Med. 115 (2019). https://doi.org/10.1016/j.compbiomed.2019.103520
Ros, D., Dai, R.: Confidence-based fall detection using multiple surveillance cameras. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2021). https://doi.org/10.1109/EMBC46164.2021.9630458
Ruiz, J.F.B., Chaparro, J.D., Peño, C.B., Llumiguano Solano, H.A., del Toro García, X., López López, J.C.: A low-cost and unobtrusive system for fall detection. In: Procedia Computer Science (2021). https://doi.org/10.1016/j.procs.2021.08.229
Ekerete, I., et al.: Fusion of unobtrusive sensing solutions for home‐based activity recognition and classification using data mining models and methods. Appl. Sci. (Switzerland) 11(19) (2021). https://doi.org/10.3390/app11199096
Zhang, Q., Karunanithi, M.: Feasibility of unobstrusive ambient sensors for fall detections in home environment. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2016). https://doi.org/10.1109/EMBC.2016.7590765
Chen, J., Kwong, K., Chang, D., Luk, J., Bajcsy, R.: Wearable sensors for reliable fall detection. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (2005). https://doi.org/10.1109/iembs.2005.1617246
Zhang, Z., Kapoor, U., Narayanan, M., Lovell, N.H., Redmond, S.J.: Design of an unobtrusive wireless sensor network for nighttime falls detection. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2011). https://doi.org/10.1109/IEMBS.2011.6091305
Jain, R., Semwal, V.B.: A novel feature extraction method for preimpact fall detection system using deep learning and wearable sensors. IEEE Sens. J. 22(23) (2022). https://doi.org/10.1109/JSEN.2022.3213814
Hsieh, C.Y., Huang, C.N., Liu, K.C., Chu, W.C., Chan, C.T.: A machine learning approach to fall detection algorithm using wearable sensor. In: Proceedings of the IEEE International Conference on Advanced Materials for Science and Engineering: Innovation, Science and Engineering, IEEE-ICAMSE 2016 (2017). https://doi.org/10.1109/ICAMSE.2016.7840209
Harris, E.J., Khoo, I.H., Demircan, E.: A survey of human gait-based artificial intelligence applications (2022). https://doi.org/10.3389/frobt.2021.749274
Song, Y.Y., Lu, Y.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psych. 27(2) (2015). https://doi.org/10.11919/j.issn.1002-0829.215044
Howcroft, J., Lemaire, E.D., Kofman, J.: Prospective elderly fall prediction by older-adult fall-risk modeling with feature selection. Biomed. Signal Process Control 43 (2018), https://doi.org/10.1016/j.bspc.2018.03.005
Saadeh, W., Butt, S.A., Bin Altaf, M.A.: A Patient-specific single sensor iot-based wearable fall prediction and detection system. IEEE Trans. Neural Syst. Rehabil. Eng. 27(5) (2019). https://doi.org/10.1109/TNSRE.2019.2911602
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Goel, A., Ekerete, I. (2024). Advanced Gait Analysis for Elder Wellbeing Monitoring. In: Zheng, H., Glass, D., Mulvenna, M., Liu, J., Wang, H. (eds) Advances in Computational Intelligence Systems. UKCI 2024. Advances in Intelligent Systems and Computing, vol 1462. Springer, Cham. https://doi.org/10.1007/978-3-031-78857-4_16
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