Abstract
Accidental fall is one of the most prevalent causes of loss of autonomy, deaths and injuries among the elderly people. Fall detection and rescue systems with the advancement of technology help reduce the loss of lives and injuries, as well as the cost of healthcare systems by providing immediate emergency services to the victims of accidental falls. The aim of this paper is to perform a systematic review of the existing sensor-based fall detection and rescue systems and to facilitate further research in this field. The systems are reviewed based on their architecture, used sensors, performance metrics, limitations, etc. This review also provides a taxonomy for classifying the fall detection systems. The systems have been divided into two main categories: single sensor-based fall detection systems, and multiple sensor-based fall detection systems. Although single sensor-based systems are very accurate in detecting falls, multiple sensor-based systems are more efficient. The low power consumption of most single sensor-based systems especially those which are based on the accelerometer is perfect for wearable solutions, while most multiple sensor-based systems are perfect for indoor monitoring.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aguiar B, Rocha T, Silva J, Sousa I (2014) Accelerometer-based fall detection for smartphones. In: 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, pp 1–6
Bagalà F, Becker C, Cappello A et al (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7:e37062. https://doi.org/10.1371/journal.pone.0037062
Bergen G, Stevens MR, Burns ER (2016) Falls and fall injuries among adults aged ≥65 years—United States, 2014. MMWR Morb Mortal Wkly Rep 65:993–998. https://doi.org/10.15585/mmwr.mm6537a2
Bet P, Castro PC, Ponti MA (2019) Fall detection and fall risk assessment in older person using wearable sensors: a systematic review. Int J Med Inform 130:103946. https://doi.org/10.1016/j.ijmedinf.2019.08.006
Bin KS, Park J-H, Kwon C et al (2019) An energy-efficient algorithm for classification of fall types using a wearable sensor. IEEE Access 7:31321–31329. https://doi.org/10.1109/ACCESS.2019.2902718
Boutellaa E, Kerdjidj O, Ghanem K (2019) Covariance matrix based fall detection from multiple wearable sensors. J Biomed Inform 94:103189. https://doi.org/10.1016/j.jbi.2019.103189
Buke A, Gaoli F, Yongcai W et al (2015) Healthcare algorithms by wearable inertial sensors: a survey. China Commun 12:1–12. https://doi.org/10.1109/CC.2015.7114054
Cao H, Wu S, Zhou Z, et al (2016) A fall detection method based on acceleration data and hidden Markov model. In: 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, pp 684–689
Casilari E, Santoyo-Ramón J-A, Cano-García J-M (2017) Analysis of public datasets for wearable fall detection systems. Sensors 17:1513. https://doi.org/10.3390/s17071513
Chaudhuri S, Thompson H, Demiris G (2014) Fall detection devices and their use with older adults. J Geriatr Phys Ther 37:178–196. https://doi.org/10.1519/JPT.0b013e3182abe779
Chen WH, Ma HP (2015) A fall detection system based on infrared array sensors with tracking capability for the elderly at home. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom). IEEE, pp 428–434
Chen L, Li R, Zhang H et al (2019) Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch. Measurement 140:215–226. https://doi.org/10.1016/j.measurement.2019.03.079
De Cillis F, De Simio F, Guido F, et al (2015) Fall-detection solution for mobile platforms using accelerometer and gyroscope data. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 3727–3730
Debes C, Merentitis A, Sukhanov S et al (2016) Monitoring activities of daily living in smart homes: understanding human behavior. IEEE Signal Process Mag 33:81–94. https://doi.org/10.1109/MSP.2015.2503881
Delahoz Y, Labrador M (2014) Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14:19806–19842. https://doi.org/10.3390/s141019806
Ding Y, Li H, Li C, et al (2017) Fall detection based on depth images via wavelet moment. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, pp 1–5
Ding C, Zou Y, Sun L, et al (2019) Fall detection with multi-domain features by a portable FMCW radar. In: 2019 IEEE MTT-S International Wireless Symposium (IWS). IEEE, pp 1–3
Doulamis N (2010) Viusal fall alert service in low computational power device to assist persons’ with dementia. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010). IEEE, pp 1–5
Erden F, Velipasalar S, Alkar AZ, Cetin AE (2016) Sensors in assisted living: a survey of signal and image processing methods. IEEE Signal Process Mag 33:36–44. https://doi.org/10.1109/MSP.2015.2489978
Erol B, Amin MG (2019) Radar data cube processing for human activity recognition using multisubspace learning. IEEE Trans Aerosp Electron Syst 55:3617–3628. https://doi.org/10.1109/TAES.2019.2910980
Florence CS, Bergen G, Atherly A et al (2018) Medical costs of fatal and nonfatal falls in older adults. J Am Geriatr Soc 66:693–698. https://doi.org/10.1111/jgs.15304
Fung NM, Wong Sing Ann J, Tung YH, et al (2019) Elderly Fall Detection and Location Tracking System Using Heterogeneous Wireless Networks. In: 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, pp 44–49
Gjoreski H, Gams M, Luštrek M (2014) Context-based fall detection and activity recognition using inertial and location sensors. J Ambient Intell Smart Environ 6:419–433. https://doi.org/10.3233/AIS-140268
Hamm J, Money AG, Atwal A, Paraskevopoulos I (2016) Fall prevention intervention technologies: a conceptual framework and survey of the state of the art. J Biomed Inform 59:319–345. https://doi.org/10.1016/j.jbi.2015.12.013
Homann B, Plaschg A, Grundner M et al (2013) The impact of neurological disorders on the risk for falls in the community dwelling elderly: a case-controlled study. BMJ Open 3:e003367. https://doi.org/10.1136/bmjopen-2013-003367
Hu L, Wang S, Chen Y, et al (2018) Fall detection algorithms based on wearable device: a review. zjujournals.com
Huynh QT, Nguyen UD, Irazabal LB et al (2015) Optimization of an accelerometer and gyroscope-based fall detection algorithm. J Sensors 2015:1–8. https://doi.org/10.1155/2015/452078
Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. Biomed Eng Online 12:66. https://doi.org/10.1186/1475-925X-12-66
Igual R, Medrano C, Plaza I (2015) A comparison of public datasets for acceleration-based fall detection. Med Eng Phys 37:870–878. https://doi.org/10.1016/j.medengphy.2015.06.009
Islam MM, Neom NH, Imtiaz MS et al (2019) A review on fall detection systems using data from smartphone sensors. Ing des Syst d’Inform 24:569–576. https://doi.org/10.18280/isi.240602
Islam MM, Rahaman A, Islam MR (2020) Development of smart healthcare monitoring system in IoT environment. SN Comput Sci 1:185. https://doi.org/10.1007/s42979-020-00195-y
Jager TE, Weiss HB, Coben JH, Pepe PE (2000) Traumatic brain injuries evaluated in U.S. emergency departments, 1992–1994. Acad Emerg Med 7:134–140. https://doi.org/10.1111/j.1553-2712.2000.tb00515.x
Jankowski S, Szymanski Z, Dziomin U, et al (2015) Deep learning classifier for fall detection based on IR distance sensor data. In: 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, pp 723–727
Jokanovic B, Amin M, Ahmad F (2016) Radar fall motion detection using deep learning. In: 2016 IEEE Radar Conference (RadarConf). IEEE, pp 1–6
Khan SS, Hoey J (2017) Review of fall detection techniques: a data availability perspective. Med Eng Phys 39:12–22. https://doi.org/10.1016/j.medengphy.2016.10.014
Kong X, Meng L, Tomiyama H (2017) Fall detection for elderly persons using a depth camera. In: 2017 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, pp 269–273
Kong X, Meng Z, Meng L, Tomiyama H (2019) Three-states-transition method for fall detection algorithm using depth image. J Robot Mechatronics 31:88–94. https://doi.org/10.20965/jrm.2019.p0088
Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Programs Biomed 117:489–501. https://doi.org/10.1016/j.cmpb.2014.09.005
Lee J-S, Tseng H-H (2019) Development of an enhanced threshold-based fall detection system using smartphones with built-in accelerometers. IEEE Sens J 19:8293–8302. https://doi.org/10.1109/JSEN.2019.2918690
Lim D, Park C, Kim NH et al (2014) Fall-detection algorithm using 3-axis acceleration: combination with simple threshold and hidden Markov model. J Appl Math 2014:1–8. https://doi.org/10.1155/2014/896030
Lord SR, Menz HB, Sherrington C (2006) Home environment risk factors for falls in older people and the efficacy of home modifications. Age Ageing 35:ii55–ii59. https://doi.org/10.1093/ageing/afl088
Martínez-Villaseñor L, Ponce H, Brieva J et al (2019) UP-fall detection dataset: a multimodal approach. Sensors 19:1988. https://doi.org/10.3390/s19091988
Mastorakis G, Makris D (2014) Fall detection system using Kinect’s infrared sensor. J Real-Time Image Process 9:635–646. https://doi.org/10.1007/s11554-012-0246-9
Mehmood A, Nadeem A, Ashraf M et al (2019) A novel fall detection algorithm for elderly using SHIMMER wearable sensors. Health Technol (Berl) 9:631–646. https://doi.org/10.1007/s12553-019-00298-4
Moulik S, Majumdar S (2019) FallSense : an automatic fall detection and alarm generation system in IoT-enabled environment. IEEE Sens J 19:8452–8459. https://doi.org/10.1109/JSEN.2018.2880739
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
Mukhopadhyay SC (2015) Wearable sensors for human activity monitoring: a review. IEEE Sens J 15:1321–1330. https://doi.org/10.1109/JSEN.2014.2370945
Nguyen Gia T, Sarker VK, Tcarenko I et al (2018) Energy efficient wearable sensor node for IoT-based fall detection systems. Microprocess Microsyst 56:34–46. https://doi.org/10.1016/j.micpro.2017.10.014
Nooruddin S, Islam M, Sharna FA (2020) Internet of things an IoT based device-type invariant fall detection system. Internet Things 9:100130. https://doi.org/10.1016/j.iot.2019.100130
Noury N, Fleury A, Rumeau P, et al (2007) Fall detection—principles and methods. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 1663–1666
O’Loughlin JL, Robitaille Y, Boivin J-F, Suissa S (1993) Incidence of and risk factors for falls and injurious falls among the community-dwelling elderly. Am J Epidemiol 137:342–354. https://doi.org/10.1093/oxfordjournals.aje.a116681
Ozcan K, Velipasalar S (2016) Wearable camera- and accelerometer-based fall detection on portable devices. IEEE Embed Syst Lett 8:6–9. https://doi.org/10.1109/LES.2015.2487241
Parkkari J, Kannus P, Palvanen M et al (1999) Majority of hip fractures occur as a result of a fall and impact on the greater trochanter of the femur: a prospective controlled hip fracture study with 206 consecutive patients. Calcif Tissue Int 65:183–187. https://doi.org/10.1007/s002239900679
Pynoos J, Steinman BA, Nguyen AQD (2010) Environmental assessment and modification as fall-prevention strategies for older adults. Clin Geriatr Med 26:633–644. https://doi.org/10.1016/j.cger.2010.07.001
Pynoos J, Steinman BA, Do Nguyen AQ, Bressette M (2012) Assessing and adapting the home environment to reduce falls and meet the changing capacity of older adults. J Hous Elderly 26:137–155. https://doi.org/10.1080/02763893.2012.673382
Rahaman A, Islam M, Islam M et al (2019) Developing IoT based smart health monitoring systems: a review. Rev d’Intell Artif 33:435–440. https://doi.org/10.18280/ria.330605
Rahman MM, Islam MM, Ahmmed S, Khan SA (2020) Obstacle and fall detection to guide the visually impaired people with real time monitoring. SN Comput Sci 1:231. https://doi.org/10.1007/s42979-020-00231-x
Rana S, Dey M, Ghavami M, Dudley S (2019) Signature inspired home environments monitoring system using IR-UWB technology. Sensors 19:385. https://doi.org/10.3390/s19020385
Ranakoti S, Arora S, Chaudhary S, et al (2019) Human Fall detection system over IMU sensors using triaxial accelerometer. In: Advances in Intelligent Systems and Computing. pp 495–507
Rand D, Eng JJ, Tang P-F et al (2009) How active are people with stroke? Stroke 40:163–168. https://doi.org/10.1161/STROKEAHA.108.523621
Rawashdeh O, Sa’deh W, Rawashdeh M, et al (2012) Development of a low-cost fall intervention system for hospitalized dementia patients. In: 2012 IEEE International Conference on Electro/Information Technology. IEEE, pp 1–7
Sadreazami H, Bolic M, Rajan S (2019) CapsFall: fall detection using ultra-wideband radar and capsule network. IEEE Access 7:55336–55343. https://doi.org/10.1109/ACCESS.2019.2907925
Sadreazami H, Bolic M, Rajan S (2020) Fall detection using standoff radar-based sensing and deep convolutional neural network. IEEE Trans Circuits Syst II Express Briefs 67:197–201. https://doi.org/10.1109/TCSII.2019.2904498
Safi K, Attal F, Mohammed S, et al (2015) Physical activity recognition using inertial wearable sensors—a review of supervised classification algorithms. In: 2015 International Conference on Advances in Biomedical Engineering, ICABME 2015
Sanchez JAU, Muñoz DM (2019) Fall detection using accelerometer on the user’s wrist and artificial neural networks. In: IFMBE Proceedings. pp 641–647
Santos GL, Endo PT, de Monteiro KH, C, et al (2019) Accelerometer-based human fall detection using convolutional neural networks. Sensors (Switzerland) 19:1–12. https://doi.org/10.3390/s19071644
Sterling DA, O’Connor JA, Bonadies J (2001) Geriatric falls: injury severity is high and disproportionate to mechanism. J Trauma Inj Infect Crit Care 50:116–119. https://doi.org/10.1097/00005373-200101000-00021
Su BY, Ho KC, Rantz MJ, Skubic M (2015) Doppler radar fall activity detection using the wavelet transform. IEEE Trans Biomed Eng 62:865–875. https://doi.org/10.1109/TBME.2014.2367038
Sugawara E, Nikaido H (2014) Properties of AdeABC and AdeIJK efflux systems of Acinetobacter baumannii compared with those of the AcrAB-TolC system of Escherichia coli. Antimicrob Agents Chemother 58:7250–7257. https://doi.org/10.1128/AAC.03728-14
Tran T-T-H, Le T-L, Morel J (2014) An analysis on human fall detection using skeleton from Microsoft kinect. In: 2014 IEEE Fifth International Conference on Communications and Electronics (ICCE). IEEE, pp 484–489
Vallabh P, Malekian R (2018) Fall detection monitoring systems: a comprehensive review. J Ambient Intell Humaniz Comput 9:1809–1833. https://doi.org/10.1007/s12652-017-0592-3
Van Thanh P, Tran D-T, Nguyen D-C et al (2019) Development of a real-time, simple and high-accuracy fall detection system for elderly using 3-DOF accelerometers. Arab J Sci Eng 44:3329–3342. https://doi.org/10.1007/s13369-018-3496-4
Verma SK, Willetts JL, Corns HL et al (2016) Falls and fall-related injuries among community-dwelling adults in the United States. PLoS ONE 11:e0150939. https://doi.org/10.1371/journal.pone.0150939
Wang J, Zhang Z, Li B et al (2014) An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans Consum Electron 60:23–29. https://doi.org/10.1109/TCE.2014.6780921
Wang P, Chen C-S, Chuan C-C (2016) Location-aware fall detection system for dementia care on nursing service in evergreen inn of Jianan Hospital. In: 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, pp 309–315
Wang Y, Wu K, Ni LM (2017) WiFall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16:581–594. https://doi.org/10.1109/TMC.2016.2557792
Ward DS, Everson KR, Vaughn A, Rodgers AB, Troiano RP (2005) Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sport Exerc 37:S582–S588. https://doi.org/10.1249/01.mss.0000185292.71933.91
Wu Y, Su Y, Hu Y et al (2019) A multi-sensor fall detection system based on multivariate statistical process analysis. J Med Biol Eng 39:336–351. https://doi.org/10.1007/s40846-018-0404-z
Xu Y, Chen J, Yang Q, Guo Q (2019) Human posture recognition and fall detection using kinect V2 camera. In: 2019 Chinese Control Conference (CCC). IEEE, pp 8488–8493
Yacchirema D, de Puga JS, Palau C, Esteve M (2019) Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers Ubiquitous Comput 23:801–817. https://doi.org/10.1007/s00779-018-01196-8
Yhdego H, Li J, Morrison S, et al (2019) towards musculoskeletal simulation-aware fall injury mitigation: transfer learning with deep CNN for fall detection. In: 2019 Spring Simulation Conference (SpringSim). IEEE, pp 1–12
Yoshino H, Moshnyaga VG, Hashimoto K (2019) Fall detection on a single doppler radar sensor by using convolutional neural networks. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, pp 2889–2892
Yu X (2008) Approaches and principles of fall detection for elderly and patient. In: 2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service, HEALTHCOM 2008
Zerrouki N, Harrou F, Sun Y, Houacine A (2016) Accelerometer and camera-based strategy for improved human fall detection. J Med Syst 40:284. https://doi.org/10.1007/s10916-016-0639-6
Zhang Z, Conly C, Athitsos V (2015) A survey on vision-based fall detection. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments—PETRA’15. ACM Press, New York, pp 1–7
Acknowledgements
This research is supported by Universiti Malaysia Pahang (UMP) through University Research Grant RDU192206.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nooruddin, S., Islam, M.M., Sharna, F.A. et al. Sensor-based fall detection systems: a review. J Ambient Intell Human Comput 13, 2735–2751 (2022). https://doi.org/10.1007/s12652-021-03248-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03248-z