A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends
Source abstract
Abstract
Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Context-Aware Activity Recognition Systems · Non-Invasive Vital Sign Monitoring · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
Faculty of Electronic Engineering (FEE), Menoufia University, Menouf 32952, Egypt
Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Faculty of Science, Sohag University, Sohag 82524, Egypt
Funding
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia
References (91)
Show source-provided references
1. (2021, April 26). Falls. World Health Organization. Available online: https://www.who.int/en/news-room/fact-sheets/detail/falls.
2. Fall Risk and Outcomes Among Patients Hospitalized with Cardiovascular Disease in the Community10.1161/CIRCOUTCOMES.117.004199 ↗
3. Wang, G., Li, Q., Wang, L., Zhang, Y., and Liu, Z. (2019). Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks. Electronics, 8.10.3390/electronics8111354 ↗
4. Rucco, R., Sorriso, A., Liparoti, M., Ferraioli, G., Sorrentino, P., Ambrosanio, M., and Baselice, F. (2018). Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors, 18.10.3390/s18051613 ↗
5. Xinguo, Y. (2008, January 7–9). Approaches and principles of fall detection for elderly and patient. Proceedings of the HealthCom 2008—10th International Conference on e-Health Networking, Applications and Services, Singapore.10.1109/HEALTH.2008.4600107 ↗
6. (2021, December 25). Google Trends. Available online: https://www.google.com/trends.
7. Advances in wearable technology and applications in physical medicine and rehabilitation10.1186/1743-0003-2-2 ↗
8. Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis10.3390/s150204193 ↗
9. Wearing and Using Personal Emergency10.3928/0098-9134-20051001-07 ↗
10. Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 9010.1136/bmj.a2227 ↗
11. Santos, G.L., Endo, P.T., Monteiro, K.H., Rocha, E.D., Silva, I., and Lynn, T. (2019). Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors, 19.10.3390/s19071644 ↗
12. Hsieh, C., Huang, C., Liu, K., Chu, W., and Chan, C. (2016, January 12–13). A machine learning approach to fall detection algorithm using wearable sensor. Proceedings of the 2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE), Chiang Mai, Thailand.10.1109/ICAMSE.2016.7840209 ↗
13. An artificial neural network–based fall detection10.1177/1847979018787905 ↗
14. Feng, P., Yu, M., Naqvi, S.M., and Chambers, J.A. (2014, January 20–23). Deep learning for posture analysis in fall detection. Proceedings of the 2014 19th International Conference on Digital Signal Processing, Hong Kong, China.10.1109/ICDSP.2014.6900806 ↗
15. Energy efficient wearable sensor node for IoT-based fall detection systems10.1016/j.micpro.2017.10.014 ↗
16. Integrating wearables with cloud-based communication for health monitoring and emergency assistance10.1016/j.iot.2018.08.004 ↗
17. Cloud-based Activity-aaService cyber–physical framework for human activity monitoring in mobility10.1016/j.future.2016.09.006 ↗
18. Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges10.1016/j.comnet.2018.09.005 ↗
19. Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge10.1016/j.ins.2020.05.070 ↗
20. Gholampooryazdi, B., Singh, I., and Sigg, S. (2017, January 24–27). 5G Ubiquitous Sensing: Passive Environmental Perception in Cellular Systems. Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, BC, Canada.10.1109/VTCFall.2017.8288261 ↗
21. Fall detection system for elderly people using IoT and ensemble machine learning algorithm10.1007/s00779-018-01196-8 ↗
22. Queralta, J.P., Gia, T.N., Tenhunen, H., and Westerlund, T. (2019, January 1–3). Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks. Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary.10.1109/TSP.2019.8768883 ↗
23. IoT Based Smart Health Monitoring with CNN Using Edge Computing
24. Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle, V., and Lundy, J.E. (2007, January 22–26). Fall detection—Principles and Methods. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.10.1109/IEMBS.2007.4352627 ↗
25. Perry, J.T., Kellog, S., Vaidya, S.M., Youn, J., Ali, H., and Sharif, H. (2009, January 28–30). Survey and evaluation of real-time fall detection approaches. Proceedings of the 2009 6th International Symposium on High Capacity Optical Networks and Enabling Technologies (HONET), Alexandria, Egypt.10.1109/HONET.2009.5423081 ↗
26. Hijaz, F., Afzal, N., Ahmad, T., and Hasan, O. (2010, January 14–16). Survey of fall detection and daily activity monitoring techniques. Proceedings of the 2010 International Conference on Information and Emerging Technologies, Karachi, Pakistan.10.1109/ICIET.2010.5625702 ↗
27. A survey on fall detection: Principles and approaches10.1016/j.neucom.2011.09.037 ↗
28. Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors10.3390/s141019806 ↗
29. Fall detection with body-worn sensors10.1007/s00391-013-0559-8 ↗
30. Zhang, Z., Conly, C., and Athitsos, V. (2015, January 1–3). A survey on vision-based fall detection. Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments, Association for Computing Machinery, Corfu, Greece. Article 46.10.1145/2769493.2769540 ↗
31. Automatic Fall Monitoring: A Review10.3390/s140712900 ↗
32. Challenges, issues and trends in fall detection systems10.1186/1475-925X-12-66 ↗
33. Fall detectors: A review of the literature10.1108/17549451211261326 ↗
34. Comparison and Characterization of Android-Based Fall Detection Systems10.3390/s141018543 ↗
35. Analysis of Android Device-Based Solutions for Fall Detection10.3390/s150817827 ↗
36. Review of fall detection techniques: A data availability perspective10.1016/j.medengphy.2016.10.014 ↗
37. A comprehensive study on the use of artificial neural networks in wearable fall detection systems10.1016/j.eswa.2019.07.028 ↗
38. Tanwar, R., Nandal, N., Zamani, M., and Manaf, A.A. (2022). Pathway of Trends and Technologies in Fall Detection: A Systematic Review. Healthcare, 10.10.3390/healthcare10010172 ↗
39. Ribeiro, O., Gomes, L., and Vale, Z. (2022). IoT-Based Human Fall Detection System. Electronics, 11.10.3390/electronics11040592 ↗
40. The state of knowledge on technologies and their use for fall detection: A scoping review10.1016/j.ijmedinf.2017.12.015 ↗
41. Practical fall detection based on IoT technologies: A survey10.1016/j.iot.2019.100124 ↗
42. Kurniawan, A., Hermawan, A.R., and Purnama, I.K.E. (2016, January 28–30). A wearable device for fall detection elderly people using tri dimensional accelerometer. Proceedings of the 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, Indonesia.10.1109/ISITIA.2016.7828740 ↗
43. A health monitoring system for vital signs using IoT10.1016/j.iot.2019.01.004 ↗
44. Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health10.1109/TCBB.2011.43 ↗
45. WiFall: Device-Free Fall Detection by Wireless Networks10.1109/TMC.2016.2557792 ↗
46. Radar and RGB-Depth Sensors for Fall Detection: A Review10.1109/JSEN.2017.2697077 ↗
47. Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos10.1109/JBHI.2016.2570300 ↗
48. Event-driven system for fall detection using body-worn accelerometer and depth sensor10.1049/iet-cvi.2017.0119 ↗
49. Fall Detection Based on Body Part Tracking Using a Depth Camera10.1109/JBHI.2014.2319372 ↗
50. Novel iBeacon Placement for Indoor Positioning in IoT10.1109/JSEN.2018.2875037 ↗
51. On the Correlation of Sensor Location and Human Activity Recognition in Body Area Networks (BANs)10.1109/JSYST.2016.2610188 ↗
52. A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System10.1109/JBHI.2014.2328593 ↗
53. A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system10.1016/j.asoc.2015.04.060 ↗
54. Rakhman, A.Z., Nugroho, L.E. (2014, January 8). Fall detection system using accelerometer and gyroscope based on smartphone. Proceedings of the 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia.10.1109/ICITACEE.2014.7065722 ↗
55. A smartphone-based fall detection system10.1016/j.pmcj.2012.08.003 ↗
56. Pan, D., Liu, H., Qu, D., and Zhang, Z. (2020). CNN-Based Fall Detection Strategy with Edge Computing Scheduling in Smart Cities. Electronics, 9.10.3390/electronics9111780 ↗
57. Cillis, F.D., Simio, F.D., Guido, F., Incalzi, R.A., and Setola, R. (2015, January 25–29). Fall-detection solution for mobile platforms using accelerometer and gyroscope data. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy.
58. F2D: A Location Aware Fall Detection System Tested with Real Data from Daily Life of Elderly People10.1016/j.procs.2016.09.035 ↗
59. Iliadis, L., Maglogiannis, I., and Papadopoulos, H. (2014). Fall Detection Using Commodity Smart Watch and Smart Phone. Proceedings of the Artificial Intelligence Applications and Innovations, Rhodes, Greece, 19–21 September 2014, Springer.10.1007/978-3-662-44654-6 ↗
60. Guo, H.W., Hsieh, Y.T., Huang, Y.S., Chien, J.C., Haraikawa, K., and Shieh, J.S. (2015, January 28–30). A threshold-based algorithm of fall detection using a wearable device with tri-axial accelerometer and gyroscope. Proceedings of the 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.10.1109/ICIIBMS.2015.7439470 ↗
61. Rezazadeh, J., Sandrasegaran, K., and Kong, X. (2018, January 5–8). A location-based smart shopping system with IoT technology. Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, Singapore.10.1109/WF-IoT.2018.8355175 ↗
62. Nari, M.I., Suprapto, S.S., Kusumah, I.H., and Adiprawita, W. (2016, January 29–30). A simple design of wearable device for fall detection with accelerometer and gyroscope. Proceedings of the 2016 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia.10.1109/ISESD.2016.7886698 ↗
63. Accelerometer-based fall detection using optimized ZigBee data streaming10.1016/j.mejo.2010.06.014 ↗
64. Impact of static trajectories on localization in wireless sensor networks10.1007/s11276-014-0821-z ↗
65. Smartphone Based Data Mining for Fall Detection: Analysis and Design10.1016/j.procs.2017.01.188 ↗
66. Smart IoT-based system for detecting RPW larvae in date palms using mixed depthwise convolutional networks10.1016/j.aej.2021.10.050 ↗
67. A pervasive neural network-based fall detection system on smart phone10.3233/AIS-150306 ↗
68. An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ann) and support vector machine (svm) algorithms10.4236/ojab.2014.34004 ↗
69. An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment10.1109/JBHI.2013.2274479 ↗
70. mPadal: A joint local-and-global multi-view feature selection method for activity recognition10.1007/s10489-014-0566-5 ↗
71. Decision tree analysis of construction fall accidents involving roofers10.1016/j.eswa.2014.10.009 ↗
72. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier10.1049/htl.2015.0018 ↗
73. Li, J., Li, M., Wang, Z., and Zhao, Q. (2015, January 6–9). An improved classification method for fall detection based on Bayesian framework. Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China.10.1109/ROBIO.2015.7418773 ↗
74. An Intelligent Information Forwarder for Healthcare Big Data Systems with Distributed Wearable Sensors10.1109/JSYST.2014.2308324 ↗
75. A robust human activity recognition system using smartphone sensors and deep learning10.1016/j.future.2017.11.029 ↗
76. Primitive activity recognition from short sequences of sensory data10.1007/s10489-018-1166-6 ↗
77. Sucerquia, A., López, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A Fall and Movement Dataset. Sensors, 17.10.3390/s17010198 ↗
78. Khojasteh, S.B., Villar, J.R., Chira, C., González, V.M., and De la Cal, E. (2018). Improving Fall Detection Using an On-Wrist Wearable Accelerometer. Sensors, 18.10.3390/s18051350 ↗
79. Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset10.1371/journal.pone.0075196 ↗
80. UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection10.1016/j.procs.2017.06.110 ↗
81. Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition10.1142/S0129065716500374 ↗
82. Comparison of acceleration signals of simulated and real-world backward falls10.1016/j.medengphy.2010.11.003 ↗
83. Nait Aicha, A., Englebienne, G., Van Schooten, K.S., Pijnappels, M., and Kröse, B. (2018). Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors, 18.10.3390/s18051654 ↗
84. Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data10.4258/hir.2017.23.3.147 ↗
85. The implementation of an intelligent and video-based fall detection system using a neural network10.1016/j.asoc.2014.01.024 ↗
86. Nukala, B.T., Nakano, T., Rodriguez, A., Tsay, J., Lopez, J., Nguyen, T.Q., Zupancic, S., and Lie, D.Y.C. (2016). Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor. Biosensors, 6.10.3390/bios6040058 ↗
87. Škoda, P., Lipić, T., Srp, Á., Rogina, B.M., Skala, K., and Vajda, F. Implementation framework for Artificial Neural Networks on FPGA. Proceedings of the 2011 Proceedings of the 34th International Convention MIPRO, Opatija, Croatia, 23–27 May 2011.
88. Huang, T., Zeng, Z., Li, C., and Leung, C.S. (2012, January 12–15). Optimization of a Neural Network for Computer Vision Based Fall Detection with Fixed-Point Arithmetic. Proceedings of the Neural Information Processing, Doha, Qatar.
89. Özdemir, A.T. (2016). An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice. Sensors, 16.10.3390/s16081161 ↗
90. Lightweight transfer learning models for ultrasound-guided classification of COVID-19 patients
91. Lin, P.-W., and Hsu, C.-M. (2021). Lightweight Convolutional Neural Networks with Model-Switching Architecture for Multi-Scenario Road Semantic Segmentation. Appl. Sci., 11.10.3390/app11167424 ↗