Skip to main content

Advertisement

SpringerLink
Log in

A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

With the advancement in the healthcare and medicine sector, now a day’s average life span of humans has increased. Due to an increase in average life expectancy, the demographic of old age people has increased. According to a World Health Organization report, old age people have more chances to get with fall and recurrent fall (World Health Organization in Who global report on falls prevention in older age, 2007). For elder people, human falls may create severe medical issues and injuries too. Because of the ever-growing old age people, there is an urgent requirement for the development of fall detection systems. Fortunately, with the help of advanced biomedical wireless sensor networks, the internet of things, Microelectromechanical sensors, and human–computer interaction it is possible to address this issue of human fall detection. In this research article, we have presented a survey on human fall detection methods and Systems. Human fall detection can be developed using one of the following ways: vision-based techniques, ambient sensor-based techniques, and wearable device-based techniques. In this review article, we have presented a brief review of the above-mentioned methods. Various machine learning methods for fall detection and activity of daily life have been discussed rigorously in this article with available literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. World Health Organization. (2007). Who global report on falls prevention in older age. Retrieved December 25, 2020, from https://www.who.int/violence injury prevention/publications/other injury/fallsprevention.pdf

  2. Center for Disease Control and Prevention. (2017). Important facts about falls. Retrieved November 25, 2020, from https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html

  3. Falls. World Health Organization. (2018). Jan 16, pp. 1–2. Retrieved December 25, 2020, from http://www.who.int/news room/fact-sheets/detail/falls

  4. James, S. L., Lucchesi, L. R., & Bisignano, C., et al. (2017). The global burden of falls: Global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017 Injury Prevention (vol. 26, pp. i3–i11). https://doi.org/10.1136/injuryprev-2019-043286

  5. Wang, J., Zhang, Z., Li, B., Lee, S., & Sherratt, R. S. (2014). An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Transactions on Consumer Electronics, 60(1), 23–29. https://doi.org/10.1109/TCE.2014.6780921.

    Article  Google Scholar 

  6. Tinetti, M. E., Liu, W. L., & Claus, E. B. (1993). Predictors and prognosis of inability to get up after falls among elderly persons. JAMA, 269(1), 65–70. https://doi.org/10.1001/jama.1993.03500010075035.

    Article  Google Scholar 

  7. Brownsell, S., & Hawley, M. S. (2004). Automatic fall detectors and the fear of falling. Journal of Telemedicine and Telecare, 10(5), 262–6. https://doi.org/10.1258/1357633042026251.

    Article  Google Scholar 

  8. Mubashir, M., Shao, L., & Seed, L. (2013). A survey on fall detection: Principles and approaches. Neurocomputing, 100, 144–152. https://doi.org/10.1016/j.neucom.2011.09.037.

    Article  Google Scholar 

  9. Nyan, M. N., Tay, F. E., & Murugasu, E. (2008). A wearable system for pre-impact fall detection. Journal of Biomechanics, 41(16), 3475–81. https://doi.org/10.1016/j.jbiomech.2008.08.009.

    Article  Google Scholar 

  10. Nyan, M. N., Tay, F. E., & Mah, M. Z. (2008). Application of motion analysis system in pre-impact fall detection. Journal of Biomechanics, 41(10), 2297–304. https://doi.org/10.1016/j.jbiomech.2008.03.042.

    Article  Google Scholar 

  11. Chin, Z. H., Ng, H., Yap, T. T. V., Tong, H. L., Ho, C. C., & Goh, V. T. (2019). Daily activities classification on human motion primitives detection dataset. In R. Alfred, Y. Lim, A. Ibrahim, P. Anthony (Eds.), Computational science and technology. Lecture notes in electrical engineering (vol. 481). Springer. https://doi.org/10.1007/978-981-13-2622-6(12)

  12. Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 790–808. https://doi.org/10.1109/tsmcc.2012.2198883

  13. Ren, L., & Peng, Y. (2019). Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 77702–77722. https://doi.org/10.1109/ACCESS.2019.2922708.

    Article  Google Scholar 

  14. El-Bendary, N., Tan, Q., Pivot, F. C., & Lam, A. (2013). Fall detection and prevention for the elderly: A review of trends and challenges. International Journal on Smart Sensing and Intelligent Systems. https://doi.org/10.21307/ijssis-2017-588

  15. Tamura, T., Yoshimura, T., Sekine, M., Uchida, M., & Tanaka, O. (2009). A wearable airbag to prevent fall injuries. IEEE Transactions on Information Technology in Biomedicine, 13(6), 910–4. https://doi.org/10.1109/TITB.2009.2033673.

    Article  Google Scholar 

  16. Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Laighin, G. O., Rialle, V., & Lundy, J. E. (2007). Fall detection-principles and methods. In Proceedings of the 29th annual international conference of the IEEE engineering in medicine and biology, Lyon, France (pp. 1663–1666). https://doi.org/10.1109/IEMBS.2007.4352627

  17. Nizam, Y., Haji Mohd, M. N., & Abdul Jamil, M. M. (2016). A study on human fall detection systems: Daily activity classification and sensing techniques. International Journal of Integrated Engineering, 8(1), 66.

    Google Scholar 

  18. Ramachandran, A., & Karuppiah, A. (2020). A survey on recent advances in wearable fall detection system. BioMed Research International, 2020, Article ID 2167160. https://doi.org/10.1155/2020/2167160

  19. Luczak, T., Saucier, D., Burch, V., Reuben, F., Ball, J. E., Chander, H., et al. (2018). Closing the wearable gap: Mobile systems for kinematic signal monitoring of the foot and ankle. Electronics, 7(7), 117. https://doi.org/10.3390/electronics7070117.

    Article  Google Scholar 

  20. Palmerini, L., Klenk, J., Becker, C., & Chiari, L. (2020). Accelerometer-based fall detection using machine learning: Training and testing on real-world falls. Sensors, 20(22), 6479. https://doi.org/10.3390/s20226479.

    Article  Google Scholar 

  21. Aphairaj, D., Kitsonti, M., & Thanapornsawan, T. (2019). Fall detection system with 3-axis accelerometer. Journal of Physics: Conference Series, 1380, 012060. https://doi.org/10.1088/1742-6596/1380/1/012060

  22. Sucerquia, A., López, J. D., & Vargas-Bonilla, J. F. (2018). Real-life/real-time elderly fall detection with a triaxial accelerometer. Sensors, 18(4), 1101. https://doi.org/10.3390/s18041101.

    Article  Google Scholar 

  23. Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and Big Data. Procedia Computer Science, 130(C), 603–610. https://doi.org/10.1016/j.procs.2018.04.110

  24. Mao, A., Ma, X., He, Y., & Luo, J. (2017). Highly portable, sensor-based system for human fall monitoring. Sensors, 17(9), 2096. https://doi.org/10.3390/s17092096.

    Article  Google Scholar 

  25. Pierleoni, P., Belli, A., Palma, L., Pellegrini, M., Pernini, L., & Valenti, S. (2015). A high reliabiliable wearable device for elderly fall detection. IEEE Sensors Journal, 15(8), 4544–4553. https://doi.org/10.1109/JSEN.2015.2423562.

    Article  Google Scholar 

  26. Pierleoni, P., Belli, A., Maurizi, L., Palma, L., Pernini, L., Paniccia, M., & Valenti, S. (2016). A wearable fall detector for elderly people based on AHRS and barometric sensor. IEEE Sensors Journal, 16, 6733–6744. https://doi.org/10.1109/JSEN.2016.2585667.

    Article  Google Scholar 

  27. Wu, F., Zhao, H., Zhao, Y., & Zhong, H. (2015). Development of a wearable-sensor-based fall detection system. International Journal of Telemedicine and Applications, 2015, Article ID 576364. https://doi.org/10.1155/2015/576364

  28. Lim, D., Park, C., Kim, N. H., Kim, S.-H., & Yu, Y. S. (2014) Fall-detection algorithm using 3-axis acceleration: Combination with simple threshold and hidden Markov model. Journal of Applied Mathematics, 2014, Article ID 896030. https://doi.org/10.1155/2014/896030

  29. Tong, L., Song, Q., Ge, Y., & Liu May, M. (2013). HMM-based human fall detection and prediction method using tri-axial accelerometer. IEEE Sensors Journal, 13(5), 1849–1856. https://doi.org/10.1109/JSEN.2013.2245231.

    Article  Google Scholar 

  30. Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., & Lovell, N. H. (2010). Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 619–627. https://doi.org/10.1109/TNSRE.2010.2070807.

    Article  Google Scholar 

  31. Chen, Y., Li, W., Wang, L., Hu, J., & Ye, M. (2020). Vision-based fall event detection in complex background using attention guided bi-directional LSTM. IEEE Access, 8, 161337–161348. https://doi.org/10.1109/ACCESS.2020.3021795.

    Article  Google Scholar 

  32. Cai, X., Li, S., Liu, X., & Han, G. (2020). Vision-based fall detection with multi-task hourglass convolutional auto-encoder. IEEE Access, 8, 44493–44502. https://doi.org/10.1109/ACCESS.2020.2978249.

    Article  Google Scholar 

  33. Lu, N., Wu, Y., Feng, L., & Song, J. (2019). Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data. IEEE Journal of Biomedical and Health Informatics, 23(1), 314–323. https://doi.org/10.1109/JBHI.2018.2808281.

    Article  Google Scholar 

  34. Harrou, F., Zerrouki, N., Sun, Y., & Houacine, A. (2019). An integrated vision-based approach for efficient human fall detection in a home environment. IEEE Access, 7, 114966–114974. https://doi.org/10.1109/ACCESS.2019.2936320.

    Article  Google Scholar 

  35. Lu, K.-L., & Chu, E.T.-H. (2018). An image-based fall detection system for the elderly. Applied Science, 8(10), 1995. https://doi.org/10.3390/app8101995.

    Article  Google Scholar 

  36. Lotfi, S., Albawendi, H., Powell, K. A., & Langensiepen, C. (2018). Supporting independent living for older adults: Employing a visual based fall detection through analysing the motion and shape of the human body. IEEE Access, 6, 70272–70282. https://doi.org/10.1109/ACCESS.2018.2881237.

    Article  Google Scholar 

  37. Gunale, K. G., & Mukherji, P. (2018). Indoor human fall detection system based on automatic vision using computer vision and machine learning algorithms. Journal of Engineering Science and Technology, 13(8), 2587–2605.

    Google Scholar 

  38. Núñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017). Vision-based fall detection with convolutional neural networks. Wireless Communications and Mobile Computing, 2017, Article ID 9474806. https://doi.org/10.1155/2017/9474806

  39. Adhikari, K., Bouchachia, H., & Nait-Charif, H. (2017). Activity recognition for indoor fall detection using convolutional neural network. In 2017 Fifteenth IAPR international conference on machine vision applications (MVA), Nagoya (pp. 81–84). https://doi.org/10.23919/MVA.2017.7986795

  40. Thuc, H. L. U., Van Tuan, P., & Hwang, J.-N. (2017). An effective video-based model for fall monitoring of the elderly. In 2017 International conference on system science and engineering (ICSSE). https://doi.org/10.1109/icsse.2017.8030835

  41. Han, T., Kang, W., & Choi, G. (2020). IR-UWB sensor based fall detection method using CNN algorithm. Sensors, 20(20), 5948. https://doi.org/10.3390/s20205948.

    Article  Google Scholar 

  42. Elshwemy, F. A., Elbasiony, R., & Saidahmed, M. T. (2020). A new approach for thermal vision-based fall detection using residual autoencoder. International Journal of Intelligent Engineering and Systems. https://doi.org/10.22266/ijies2020.0430.24https://doi.org/10.22266/ijies2020.0430.24

  43. Xi, X., Jiang, W., Lü, Z., Miran, S. M., & Luo, Z.-Z. (2020). Daily activity monitoring and fall detection based on surface electromyography and plantar pressure. Complexity, 2020, 9532067. https://doi.org/10.1155/2020/9532067

  44. Clemente, J., Li, F., Valero, M., & Song, W. (2020). Smart seismic sensing for indoor fall detection, location, and notification. IEEE Journal of Biomedical and Health Informatics, 24(2), 524–532. https://doi.org/10.1109/JBHI.2019.2907498.

    Article  Google Scholar 

  45. Ma, L., Liu, M., Wang, N., Wang, L., Yang, Y., & Wang, H. (2020). Room-level fall detection based on ultra-wideband (UWB) monostatic radar and convolutional long short-term memory (LSTM). Sensors, 20(4), 1105. https://doi.org/10.3390/s20041105.

    Article  Google Scholar 

  46. Chelli, A., & Pätzold, M. (2019). A machine learning approach for fall detection based on the instantaneous Doppler frequency. IEEE Access, 7, 166173–166189. https://doi.org/10.1109/ACCESS.2019.2947739.

    Article  Google Scholar 

  47. Taramasco, C., et al. (2018). A novel monitoring system for fall detection in older people. IEEE Access, 6, 43563–43574. https://doi.org/10.1109/ACCESS.2018.2861331.

    Article  Google Scholar 

  48. Wang, H., Zhang, D., Wang, Y., Ma, J., Wang, Y., & Li, S. (2017). RT-Fall: A real-time and contactless fall detection system with commodity WiFi devices. IEEE Transactions on Mobile Computing, 16(2), 511–526. https://doi.org/10.1109/tmc.2016.2557795.

    Article  Google Scholar 

  49. Wang, Y., Wu, K., & Ni, L. M. (2017). WiFall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 16(2), 581–594. https://doi.org/10.1109/TMC.2016.2557792.

    Article  Google Scholar 

  50. Hayashida, A., Moshnyaga, V., & Hashimoto, K. (2017). The use of thermal ir array sensor for indoor fall detection. In 2017 IEEE international conference on systems, man, and cybernetics (SMC). https://doi.org/10.1109/smc.2017.8122671

  51. Dias, P. V. G. F. , Costa, E. D. M., Tcheou, M. P., & Lovisolo, L. (2016). Fall detection monitoring system with position detection for elderly at indoor environments under supervision. In 2016 8th IEEE Latin-American conference on communications (LATINCOM), Medellin (pp. 1–6). https://doi.org/10.1109/LATINCOM.2016.7811576.

  52. Wild, K., Boise, L., Lundell, J., & Foucek, A. (2008). Unobtrusive in-home monitoring of cognitive and physical health: Reactions and perceptions of older adults. Journal of Applied Gerontology, 27, 181–200. https://doi.org/10.1177/0733464807311435.

    Article  Google Scholar 

  53. 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. https://doi.org/10.1016/j.cmpb.2014.09.005.

    Article  Google Scholar 

  54. Liu, J., Luo, J., & Shah, M. (2009) Recognizing realistic actions from videos “in the wild”. In 2009 IEEE conference on computer vision and pattern recognition, Miami, FL (pp. 1996–2003). https://doi.org/10.1109/CVPR.2009.5206744

  55. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011)HMDB: A large video database for human motion recognition. In 2011 International conference on computer vision, Barcelona (pp. 2556–2563). https://doi.org/10.1109/ICCV.2011.6126543

  56. Charfi, I., Miteran, J., Dubois, J., Atri, M., & Tourki, R. (2013). Optimized spatio-temporal descriptors for real-time fall detection: Comparison of support vector machine and Adaboost-based classification. Journal of Electronic Imaging, 22(4), 041106. https://doi.org/10.1117/1.JEI.22.4.041106.

    Article  Google Scholar 

  57. Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2010). Multiple cameras fall dataset, Technical report 1350, DIRO—Universitè de Montrèal.

  58. Ngo, Y. T., Nguyen, H. V., & Pham, T. V. (2012). Study on fall detection based on intelligent video analysis. In The 2012 International conference on advanced technologies for communications, Hanoi (pp. 114–117). https://doi.org/10.1109/ATC.2012.6404242

  59. Wang, X., Ellul, J., & Azzopardi, G. (2020). Elderly Fall Detection Systems: A Literature Survey. Front. Robot. AI, 7, 71. https://doi.org/10.3389/frobt.2020.00071.

    Article  Google Scholar 

  60. Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C. (2019). UP-fall detection dataset: A multimodal approach. Sensors, 19(9), 1988. https://doi.org/10.3390/s19091988.

    Article  Google Scholar 

  61. Martínez-Villaseñor, L., Ponce, H., & Espinosa-Loera, R. A. (2018). Multimodal database for human activity recognition and fall detection. Proceedings, 2(19), 1237. https://doi.org/10.3390/proceedings2191237

  62. Li, H., Shrestha, A., Heidari, H., Le Kernec, J., & Fioranelli, F. (2020). Bi-LSTM network for multimodal continuous human activity recognition and fall detection. IEEE Sensors Journal, 20(3), 1191–1201. https://doi.org/10.1109/JSEN.2019.2946095.

    Article  Google Scholar 

  63. Martínez-Villaseñor, L., Ponce, H., & Perez-Daniel, K. (2019). Deep learning for multimodal fall detection. In IEEE international conference on systems, man and cybernetics (SMC), Bari, Italy (pp. 3422–3429). https://doi.org/10.1109/SMC.2019.8914429

  64. Frank, K., Vera Nadales, M. J., Robertson, P., & Pfeifer, T. Bayesian recognition of motion related activities with inertial sensors. In Proceedings of the 12th ACM international conference adjunct papers on ubiquitous computing-adjunct; Copenhagen, Denmark, 26–29 September 2010 (pp. 445–446). https://doi.org/10.1145/1864431.1864480

  65. Vavoulas, G., Pediaditis, M., Spanakis, E., & Tsiknakis, M. (2013). The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones. In 6th IEEE international symposium on monitoring and surveillance research (ISMSR): Healthcare-bioinformatics, Chania, Greece. https://doi.org/10.1109/BIBE.2013.6701629

  66. Casilari, E., Santoyo-Ramón, J. A., & Cano-García, J. M. (2017). UMAFall: A multisensor dataset for the research on automatic fall detection. Procedia Computer Science, 110, 32-si39. https://doi.org/10.1016/j.procs.2017.06.110

  67. Medrano, C., Igual, R., Plaza, I., & Castro, M. (2014). Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE, 9(4), e94811. https://doi.org/10.1371/journal.pone.0094811.

    Article  Google Scholar 

  68. Zhang, Z., Conly, C., & Athitsos, V. (2014). Evaluating depth-based computer vision methods for fall detection under occlusions. In G. Bebis, et al. (Eds.), Advances in visual computing (ISVC 2014). Lecture notes in computer science (vol. 8888). Springer. https://doi.org/10.1007/978-3-319-14364-4_19

  69. Charfi, I., Miteran, J., Dubois, J., Atri, M., & Tourki, R. (2012). Definition and performance evaluation of a robust SVM-based fall detection solution. SITIS, 12, 218–224. https://doi.org/10.1109/SITIS.2012.155.

    Article  Google Scholar 

  70. Mastorakis, G., & Makris, D. (2014). Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing, 9, 635–646. https://doi.org/10.1007/s11554-012-0246-9.

    Article  Google Scholar 

  71. Teleimmersion Lab. (2013). University of California Berkeley Multimodal Human Action Database (MHAD). Retrieved September 26, 2020, fromhttp://tele-immersion.citris-uc.org/berkeley_mhad

  72. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R. (2013). Berkeley MHAD: A comprehensive multimodal human action database. In Proceedings of the 2013 IEEE workshop on applications of computer vision (WACV), Clearwater Beach, FL, USA, 15–17 January 2013 (pp. 53–60)

  73. Parikh, R., Mathai, A., Parikh, S., Chandra Sekhar, G., & Thomas, R. (2008). Understanding and using sensitivity, specificity and predictive values. Indian Journal of Ophthalmology, 56(1), 45–50. https://doi.org/10.4103/0301-4738.37595.

    Article  Google Scholar 

  74. Igual, R., Medrano, C., & Plaza, I. (2013). Challenges, issues and trends in fall detection systems. Biomedical Engineering Online, 12, 66. https://doi.org/10.1186/1475-925X-12-66.

    Article  Google Scholar 

  75. Xu, T., Zhou, Y., & Zhu, J. (2018). New advances and challenges of fall detection systems: A survey. Applied Science, 8(3), 418. https://doi.org/10.3390/app8030418.

    Article  Google Scholar 

  76. AlZubi, H. S., Gerrard-Longworth, S., Al-Nuaimy, W., Goulermas, Y., & Preece, S. (2014). Human activity classification using a single accelerometer. In 14th UK Workshop on Computational Intelligence (UKCI), Bradford, UK (pp. 1-6). https://doi.org/10.1109/UKCI.2014.6930189

  77. Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A review of human activity recognition methods. Frontiers in Robotics and AI, 2, 28. https://doi.org/10.3389/frobt.2015.00028.

    Article  Google Scholar 

Download references

Funding

This research work is not funded by any agency.

Author information

Authors and Affiliations

  1. Gujarat Technological University (GTU), Chandkheda, Ahmedabad, India

    Rohit Parmar

  2. E.C. Department, G. H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Samir Trapasiya

Authors
  1. Rohit Parmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samir Trapasiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rohit Parmar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parmar, R., Trapasiya, S. A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People. Wireless Pers Commun 126, 1679–1703 (2022). https://doi.org/10.1007/s11277-022-09816-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09816-6

Keywords

Search

Navigation