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Review

Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review

ORCID Icon, , &
Pages 35-53 | Published online: 13 Jan 2022

References

  • Population structure and ageing. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_structure_and_ageing. Accessed May 25, 2021.
  • WHO. World report on ageing and health 2015. Available from: http://www.who.int/ageing/events/world-report-2015-launch/en/. Accessed May 25, 2021.
  • World Report on Disability. Available from: https://www.who.int/teams/noncommunicable-diseases/sensory-functions-disability-and-rehabilitation/world-report-on-disability. Accessed May 25, 2021.
  • International Classification of Functioning, Disability and Health (ICF). Available from: https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health. Accessed May 25, 2021.
  • Vejux J, Ben-Sadoun G, Piolet D, Bernat V, Ould-Aoudia V, Berrut G. [Screening risk and protective factors of nursing home admission]. Geriatr Psychol Neuropsychiatr Vieil. 2019;17(1):39–50. doi:10.1684/pnv.2019.0784. French.
  • Campbell AJ, Reinken J, Allan BC, Martinez GS. Falls in old age: a study of frequency and related clinical factors. Age Ageing. 1981;10(4):264–270. doi:10.1093/ageing/10.4.264
  • Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society. Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. 2011;59(1):148–157. doi:10.1111/j.1532-5415.2010.03234.x
  • Wang X, Ellul J, Azzopardi G. Elderly Fall Detection Systems: a Literature Survey. Front Robot AI. 2020;7:71. doi:10.3389/frobt.2020.00071
  • Igual R, Medrano C, Plaza I. Challenges, issues and trends in fall detection systems. Biomed Eng Online. 2013;12(1):66. doi:10.1186/1475-925X-12-66
  • Mubashir M, Shao L, Seed L. A survey on fall detection: principles and approaches. Neurocomputing. 2013;100:144–152. doi:10.1016/j.neucom.2011.09.037
  • Qi J, Yang P, Waraich A, Deng Z, Zhao Y, Yang Y. Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic review. J Biomed Inform. 2018;87:138–153. doi:10.1016/j.jbi.2018.09.002
  • Singh K, Rajput A, Sharma S. Human Fall Detection Using Machine Learning Methods: a Survey. Int J Math, Eng, Manag Sci. 2019;5(1):161–180. doi:10.33889/IJMEMS.2020.5.1.014
  • Islam MM, Tayan O, Islam MR, et al. Deep Learning Based Systems Developed for Fall Detection: a Review. IEEE Access. 2020;8:166117–166137. doi:10.1109/ACCESS.2020.3021943
  • Islam M, Neom N, Imtiaz M, Nooruddin S, Islam M, Islam M. A Review on Fall Detection Systems Using Data from Smartphone Sensors. ISI. 2019;24(6):569–576. doi:10.18280/isi.240602
  • Rahman MM, Islam M, Ahmmed S, Khan SA. Obstacle and Fall Detection to Guide the Visually Impaired People with Real Time Monitoring. SN Comput Sci. 2020;1(4):219. doi:10.1007/s42979-020-00231-x
  • Ali Hashim H, Mohammed SL, Gharghan SK. Accurate fall detection for patients with Parkinson’s disease based on a data event algorithm and wireless sensor nodes. Measurement. 2020;156:107573. doi:10.1016/j.measurement.2020.107573
  • Ren L, Peng Y. Research of Fall Detection and Fall Prevention Technologies: a Systematic Review. IEEE Access. 2019;7:77702–77722. doi:10.1109/ACCESS.2019.2922708
  • Ko M, Kim S, Kim M, Kim K, Novel A. Approach for Outdoor Fall Detection Using Multidimensional Features from A Single Camera. Applied Sciences. 2018;8(6):984. doi:10.3390/app8060984
  • Nooruddin S, Milon islam MD, Sharna FA. An IoT based device-type invariant fall detection system. Internet of Things. 2020;9:100130. doi:10.1016/j.iot.2019.100130
  • Fehling P, Dassen T. A critical systematic review and synopsis of the alignment of scientific developments in surveillance technology in nursing care facilities. J Nurs. 2017;4(1):1. doi:10.7243/2056-9157-4-1
  • Sixsmith A, Johnson N. A smart sensor to detect the falls of the elderly. IEEE Pervasive Computing. 2004;3(2):42–47. doi:10.1109/MPRV.2004.1316817
  • Mashiyama S, Hong J, Ohtsuki T. A fall detection system using low resolution infrared array sensor. 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC); 2014: 2109–2113. doi:10.1109/PIMRC.2014.7136520.
  • Mashiyama S, Hong J, Ohtsuki T. Activity recognition using low resolution infrared array sensor. 2015 IEEE International Conference on Communications (ICC); 2015: 495–500. doi:10.1109/ICC.2015.7248370.
  • Liang Q, Yu L, Zhai X, Wan Z, Nie H. Activity Recognition Based on Thermopile Imaging Array Sensor. 2018 IEEE International Conference on Electro/Information Technology (EIT); 2018: 0770–0773. doi:10.1109/EIT.2018.8500177.
  • Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71
  • Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. doi:10.1186/s13643-016-0384-4
  • Adolf J, Macas M, Lhotska L, Dolezal J. Deep neural network based body posture recognitions and fall detection from low resolution infrared array sensor. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2018: 2394–2399. doi:10.1109/BIBM.2018.8621582.
  • Chen W-H, Ma H-P. A fall detection system based on infrared array sensors with tracking capability for the elderly at home. 2015 17th International Conference on E-Health Networking, Application Services (HealthCom); 2015: 428–434. doi:10.1109/HealthCom.2015.7454538.
  • Chen Z, Wang Y. Infrared–ultrasonic sensor fusion for support vector machine–based fall detection. J Intell Mater Syst Struct. 2018;29(9):2027–2039. doi:10.1177/1045389X18758183
  • Fan X, Zhang H, Leung C, Shen Z. Robust unobtrusive fall detection using infrared array sensors. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI); 2017: 194–199. doi:10.1109/MFI.2017.8170428.
  • Fan X, Zhang H, Leung C, Shen Z. Fall Detection with Unobtrusive Infrared Array Sensors. In: Lee S, Ko H, Oh S editors. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System. Lecture Notes in Electrical Engineering. Cham: Springer International Publishing; 2018:253–267. doi:10.1007/978-3-319-90509-9_15
  • Gochoo M, Tan T, Batjargal T, Seredin O, Huang S. Device-Free Non-Privacy Invasive Indoor Human Posture Recognition Using Low-Resolution Infrared Sensor-Based Wireless Sensor Networks and DCNN. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2018: 2311–2316. doi:10.1109/SMC.2018.00397.
  • Hayashida A, Moshnyaga V, Hashimoto K. The use of thermal ir array sensor for indoor fall detection. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2017: 594–599. doi:10.1109/SMC.2017.8122671.
  • Hayashida A, Moshnyaga V, Hashimoto K. New approach for indoor fall detection by infrared thermal array sensor. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS); 2017: 1410–1413. doi:10.1109/MWSCAS.2017.8053196.
  • Liu Z, Yang M, Yuan Y, Chan KY. Fall Detection and Personnel Tracking System Using Infrared Array Sensors. IEEE Sens J. 2020;20(16):9558–9566. doi:10.1109/JSEN.2020.2988070
  • Ogawa Y, Naito K. Fall detection scheme based on temperature distribution with IR array sensor. 2020 IEEE International Conference on Consumer Electronics (ICCE); 2020: 1–5. doi:10.1109/ICCE46568.2020.9043000.
  • Shelke S, Aksanli B. Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces. Sensors. 2019;19(4):804. doi:10.3390/s19040804
  • Taniguchi Y, Nakajima H, Tsuchiya N, Tanaka J, Aita F, Hata Y. A falling detection system with plural thermal array sensors. 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS); 2014: 673–678. doi:10.1109/SCIS-ISIS.2014.7044834.
  • Tao L, Volonakis T, Tan B, Zhang Z, Jing Y, Smith M. 3D convolutional neural network for home monitoring using low resolution thermal-sensor array. 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019); 2019:1–6. doi:10.1049/cp.2019.0100.
  • Tao L, Volonakis T, Tan B, Jing Y, Chetty K, Smith M. Home Activity Monitoring using Low Resolution Infrared Sensor. arXiv:181105416 [cs]; 2018. Available from: http://arxiv.org/abs/1811.05416. Accessed March 8, 2021.
  • Taramasco C, Rodenas T, Martinez F, et al. A Novel Monitoring System for Fall Detection in Older People. IEEE Access. 2018;6:43563–43574. doi:10.1109/ACCESS.2018.2861331
  • Taramasco C, Lazo Y, Rodenas T, Fuentes P, Martínez F, Demongeot J. System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks. J Med Syst. 2020;44(2):50. doi:10.1007/s10916-019-1484-1
  • Gharghan SK, Mohammed SL, Al-Naji A, et al. Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network. Energies. 2018;11(11):2866. doi:10.3390/en11112866
  • Pang I, Okubo Y, Sturnieks D, Lord SR, Brodie MA. Detection of Near Falls Using Wearable Devices: a Systematic Review. J Geriatr Phys Ther. 2019;42(1):48–56. doi:10.1519/JPT.0000000000000181
  • Chen Z, Liu H, Wang Y, Wang Y. A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection. American Society of Mechanical Engineers Digital Collection; 2017. doi:10.1115/SMASIS2017-3882.
  • Asbjørn D, Jim T. Recognizing Bedside Events Using Thermal and Ultrasonic Readings. Sensors. 2017;17(6):1342. doi:10.3390/s17061342
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. arXiv:151200567; 2015. Available from: http://arxiv.org/abs/1512.00567. Accessed May 25, 2021.