By definition, most of the projects involving wearable devices placed their sensor onto their subject and tested them either in a simulated or real-world environment (Table 1). Many papers attempted to identify a fall by impact, although there were also papers whose aim was to detect a fall preimpact. When measuring impact, one has to measure the vibration of the impact through the body that could cause some inaccuracies. By measuring falls preimpact, one is able to avoid this as well as any scenario where the device is damaged because of the fall. Also, by measuring falls preimpact, it may be possible in the future to prevent falling injuries by using additional equipment such as airbags that would inflate right before the fall. Some of the wearable device projects compared the preimpact fall-detection capabilities of their system with those of a camera system.35–37,76 These projects were only using camera systems as a tool for comparison and thus were not listed under multiple sensors. Another example of such a project compared the accuracy of a cell phone with the accuracy of a device solely used for fall detection.69
About 19% of the wearable projects reported utilizing older adults to test their devices in a controlled environment while only 7.1% reported monitoring older adults under real-world settings.24,32,33,70,100 The rest of the studies mostly used healthy young subjects who were volunteers, actors, or participants in the study. Thirty-five of the projects used a single device, while 4 projects used 2 separate devices and another 4 projects used 3 separate devices. The most common location for these devices was the trunk of the body (chest, waist, thorax, etc). Other devices were placed near the head, arms, hands, or feet of the subject. Systems with the device centering on the trunk had a median sensitivity of 97.5% (range, 81-100) and a median specificity of 96.9% (range, 77-100). Those involving multiple sensors had a median sensitivity of 93.4% (range, 92.5-94.2) and a median specificity of 99.8% (range, 99.3-100). Finally, the devices placed around the arm, hands, ears, or feet had a lower median sensitivity and specificity (81.5% [range, 70.4-100] and 83% [range, 80-95.7], respectively) when compared with other sensors. Median accuracy was not available for all 3 categories of sensors and thus is not provided here.
Nonwearable devices were often set up in a room where the subjects would either walk around or live in for some amount of time (Table 2). While some real-world applications of these projects exist, surprisingly there were no projects that explicitly stated using older adult subjects even in a controlled setting. The most common nonwearable systems involved cameras or motion detectors. These 2 device types are grouped together as it can be hard to differentiate them on the basis of the descriptions given by the researcher. Usually, a motion detector involved infrared sensors that identify motion, while cameras provided full images. Most of the projects used single cameras in their trials although 4 did specifically state that they used multiple camera networks.85,92 Most of the cameras were stand-alone; however, 1 study did require the subjects to wear reflective sensors on their body so that the camera could better identify them.92 The median accuracy for cameras was 96.6% (range, 77–100) while the median sensitivity and specificity were 93% (range, 66.7–100) and 98.5% (range, 87.5–100), respectively.
All 4 of the microphones systems used a robust array of microphone system, FADE, which was able to detect the 3-D sound source location.29,30,88,93 Of these 4 projects, a single project reported an accuracy of 100%, 2 reported sensitivities of 100%, and 1 reported a specificity of 97%. The 2 floor sensors listed in this category have median sensitivities and specificities of 95.4% (range, 90.7–100).31,94 However, floor sensors were generally used in combination with other sensors.
Thirty-one papers did not provide information on sensitivity, specificity, or accuracy of the fall-detection systems under study. These papers described either various design implementations of a system or results from various focus groups, case studies, interviews, or observational studies on a fall-detection device. Twenty-two papers focused on the design of their devices describing in detail how the device works, how it is to be used, and/or various methods for identifying falls. Of these designs, 11 devices were wearable with 1 even featuring a preemptive airbag.106–116 Other devices involved wireless motion sensors or cameras117–125 and phone applications.126,127
Two papers used their fall-detection devices in comparative studies. One compared the acceleration of simulated falls with that of real-world falls.128 They found many similarities between real-life falls of older adults and experimental falls of middle-aged subjects although some characteristics from experimental falls were not detectable in real-life falls. The other study compared residential communities with and without a fall-detection system. Outcomes of interest were incident falls, hospitalizations, changes in needed level of care, and resident attrition.129 The authors found that there were fewer falls per week, fewer weekly hospitalizations per week, and a higher resident retention rate at the facility with the fall-detection device.
The remaining 7 papers used various methodologies to elicit feedback from subjects on the feasibility of emerging or existing fall-detection devices. Two studies used focus groups or questionnaires to help guide the development of a new fall-detection device by suggesting various design specifications for their sensor systems.130,131 Another study used volunteers to gauge the feasibility of using a carpet sensor.132 Other studies were more interested in the perceptions of older adults regarding fall-detection devices. One study conducted a trial of an extended fall detection system versus a standard pendant alarm and interviewed the subjects after the trial.133 Older adults found that the use of telemonitoring gave them a greater sense of security and enabled them to remain at home. However, some found the device intrusive and did not feel that they were in control of alerting the call center. Another study used structured interviews to look at older adults' perceptions of having a video monitoring system in their home.134 While they reported that 96% of their participants felt favorably toward the system, only 48% said that they would actually use it. Another paper showed various groups of subjects' videos of different types of falls.135 They then proceeded to discuss the issues of falling and system designs with the subjects. Many of the subjects stated their desire for more passive fall-detection systems and most wanted to have the ability to know exactly what the system was doing at all times. The final paper described the results of focus groups and a pilot study.136 The focus groups discussed the potential for fall technologies with both adult users and health care providers, revealing that neither groups were all that receptive to the idea of fall detectors. The pilot study was used to gain insight into the effect of fall detectors on fear of falling. In this study, they measure the participants' fear of falling using the Falls Efficacy Scale pre- and posttest. They found that the use of a detector did reduce the level of fear for 1 group but this reduction was not significant.
An extensive body of work has been conducted in the area of fall detection using various devices. These devices can measure different aspects of the fall from velocity to impact and even the posture of the participant with history of falls. Each type of device appears to have its own strengths coupled with certain weaknesses.
Wearable devices, for example, if used properly, are always with their subjects and can easily detect the acceleration or impact experienced by the subjects. However, these devices are reliant on the subject not only remembering to wear the device but also choosing to wear the device that can be especially difficult at nighttime.16,41,84,105,106 These devices are also dependent on battery power and can suffer from false alarms due to impact or changes in acceleration not caused by falls. Nonwearable systems, on the contrary, do not rely on the subject to remember to use the system. Instead, they are able to survey a certain area while hardly affecting the individual. However, these systems are limited to a specific space and suffer from aspects of privacy concerns.28,84 Cameras, with their ability to take full photos or videos of their subjects, have been seen as too intrusive. These systems suffer from problems with occlusion (having the subject blocked by another object in the room) and being limited to indoor locations.40 One solution to both these issues is using multiple sensors to account for the weaknesses in each device. For example, coupling a passive camera system with a wearable system would account for the subject leaving the space of the camera or the subject forgetting to wear the device at night. However, adding more and more devices could overwhelm older adults causing them to reject such systems.
Studies have shown that older adults want to be able to live at home and are more or less willing to accept new technologies that support their independence.136,137 When dealing with fall-detection technologies, many studies have shown that older adults are favorable to such systems and find that the use of these devices can give them a greater sense of security.133–136 At the same time, however, some older adults found such devices intrusive, were annoyed by false alarms, and stated their desire for more passive systems along with an ability to know what the system was doing at all times.24 The challenge in this area of work is to create highly accurate devices that are as unobtrusive as possible. From this literature review, it appears that the technology is becoming more available to accomplish such a task. What is needed now is further testing of such devices in real-world settings.
As our review and previously published literature suggest, very few long-term real-world tests of such devices have been documented.24,32,33,43,102,128,138,139 Multiple commercial fall-detection devices are publicly available, but their accuracy is hard to identify. Real-world tests can be difficult as they can often take a large amount of resources and time. It may also be difficult to recruit for such studies, as older adults at risk of falling may also be more likely to be cognitively impaired or have a shorter life span.140 Such difficulties were experienced in a recent study by Gietzelt et al,102 who noted of 3 subjects it was possible to interview only 1. This was because of a death of a subject and the other subject developing a significantly impaired cognitive status that precluded interview.
One way to ease the challenge of real-world testing may be to expand eligibility criteria allowing for healthier older adults to join the study. However, this reduction could also be a disadvantage as it may result in fewer fall events. Boyle and Karunanithi54 tried to use real-time data with 15 adults over the course of 300 days and was only able to record 4 falls during that time. Real-world tests, however, have been shown to be a more rigorous indicator of the device's accuracy than simulated testing.100,138,139 Even with the aforementioned challenges, more real-world tests are needed to prove the efficiency of these devices and to improve the health of the individuals these devices are made for. Suggestions for future research that may overcome these challenges include careful selection of subjects to include individuals most likely to benefit from the devices, those at high risk for falls. This includes community-dwelling older adults with a fall in the previous year, or those with gait or balance disturbances that put them at high risk for fall.
Adding more real-world testing may make it more difficult to standardize the evaluation process of such devices; however, it is difficult to compare the various measurements of accuracy between devices as there is no common method for evaluating such devices. As has already been suggested, evaluating fall-detection devices needs to become more standardized to be able to properly evaluate the strengths and weaknesses of the currently available devices.16 One way to do this would be to have a subject live in a simulated environment for a certain period of time; this would allow for standardization across subjects while still providing real-world data.
This review was limited to articles written in English and indexed in PubMed, CINAHL, EMBASE, or PsycINFO and as such may have omitted other relevant published studies. Also, as with any systematic literature review, manual paper screening and review processes may have been subject to interpretive bias.
1. Lord SR, Sherrington C, Menz HB. Falls in Older People: Risk Factors and Strategies for Prevention. Cambridge, UK: Cambridge University Press; 2001.
2. Tinetti ME. Clinical practice. Preventing falls in elderly
persons. N Engl J Med. 2003;348(1):42–49.
3. Stevens JA, Mack KA, Paulozzi LJ, Ballesteros MF. Self-reported falls and fall related injuries among persons aged ≥ 65 years. J Safety Res. 2008;39(3):345–349.
4. Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil. 2001;82(8):1050–1056.
5. Tinetti ME. Prevention of falls and fall injuries in elderly
persons: a research agenda. Prev Med. 1994;23(5):756–762.
8. Ozcan A, Donat H, Gelecek N, Ozdirenc M, Karadibak D. The relationship between risk factors for falling
and the quality of life in older adults. BMC Public Health. 2005;5:90.
9. Sattin RW, Lambert Huber DA, DeVito CA, et al. The incidence of fall injury events among the elderly
in a defined population. Am J Epidemiol. 1990;131(6):1028–1037.
10. Wild D, Nayak US, Isaacs B. How dangerous are falls in old people at home? Br Med J (Clin Res Ed). 1981;282(6260):266–268.
11. Fleming J, Brayne C. Inability to get up after falling
, subsequent time on floor, and summoning help: prospective cohort study in people over 90. BMJ. 2008;337:a2227.
12. Feder G, Cryer C, Donovan S, Carter Y. Guidelines for the prevention of falls in people over 65. The Guidelines' Development Group. BMJ. 2000;321(7267):1007–1011.
13. Gillespie LD, Gillespie WJ, Robertson MC, Lamb SE, Cumming RG, Rowe BH. Interventions for preventing falls in elderly
people. Cochrane Database Syst Rev. 2003;(4):CD000340.
14. Campbell AJ, Robertson MC, Gardner MM, Norton RN, Tilyard MW, Buchner DM. Randomised controlled trial of a general practice programme of home based exercise to prevent falls in elderly
women. BMJ. 1997;315(7115):1065–1069.
15. Porter EJ. Wearing and using personal emergency response system buttons. J Gerontol Nurs. 2005;31(10):26–33.
16. Noury N, Fleury A, Rumeau P, et al. Fall detection—principles and methods. Conf Proc IEEE Eng Med Biol Soc. 2007:1663–1666.
17. Ward G, Holliday N, Fielden S, Williams S. Fall detectors: a review of the literature. J Assist Technol. 2012;6(3):202–215.
18. Stewart LSP, McKinstry B. Fear of falling
and the use of telecare by older people. Br J Occup Ther. 2012;75(7):304–312.
19. Ganyo M, Dunn M, Hope T. Ethical issues in the use of fall detectors. Ageing Soc. 2011;31(8):1350–1367.
20. Talmon J, Ammenwerth E, Brender J, de Keizer N, Nykänen P, Rigby M. STARE-HI—Statement on reporting of evaluation studies in Health Informatics. Int J Med Inform. 2009;78(1):1–9.
21. de Keizer NF, Talmon J, Ammenwerth E, Brender J, Nykanen P, Rigby M. Mini Stare-HI: guidelines for reporting health informatics evaluations in conference papers. Stud Health Technol Inform. 2010;160(Pt 2):1206–1210.
22. de Keizer NF, Talmon J, Ammenwerth E, Brender J, Rigby M, Nykanen P. Systematic prioritization of the STARE-HI reporting items. An application to short conference papers on health informatics evaluation. Methods Inf Med. 2012;51(2):104–111.
23. Bourke AK, O'Brien JV, Lyons GM. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture. 2007;26(2):194–199.
24. Bourke AK, van de Ven PW, Chaya AE, OLaighin GM, Nelson J. Testing of a long-term fall detection system incorporated into a custom vest for the elderly
. Conf Proc IEEE Eng Med Biol Soc. 2008:2844–2847.
25. Kangas M, Vikman I, Wiklander J, Lindgren P, Nyberg L, Jämsä T. Sensitivity and specificity of fall detection in people aged 40 years and over. Gait Posture. 2009;29(4):571–574.
26. Belshaw M, Taati B, Giesbercht D, Mihailidis A. Intelligent vision-based fall detection system: preliminary results from a real world deployment. Paper presented at: RESNA/ICTA 2011: Proceedings Advancing Rehabilitation Technologies for an Aging Society; June 5-8, 2011; Toronto, Ontario, Canada.
27. Belshaw M, Taati B, Snoek J, Mihailidis A. Towards a single sensor passive solution for automated fall detection. Conf Proc IEEE Eng Med Biol Soc. 2011;1773–1776.
28. Sixsmith A, Johnson N. A smart sensor to detect the falls of the elderly
. IEEE Pervasive Comput, IEEE. 2004;3(2):42–47.
29. Li Y, Zeng Z, Popescu M, Ho KC. Acoustic fall detection using a circular microphone array. Conf Proc IEEE Eng Med Biol Soc. 2010;2242–2245.
30. Popescu M, Li Y, Skubic M, Rantz M. An acoustic fall detector system that uses sound height information to reduce the false alarm rate. Conf Proc IEEE Eng Med Biol Soc. 2008;4628–4631.
31. Alwan M, Rajendran PJ, Kell S, et al. A smart and passive floor-vibration based fall detector for elderly
. In: 2nd International Conference on Information and Communication Technologies; Vol. 1; April 24-28, 2006; Damascus, Syria.
32. Bourke AK, van de Ven P, Gamble M, et al. Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities. Conf Proc IEEE Eng Med Biol Soc. 2010;2782–2785.
33. Bourke AK, van de Ven P, Gamble M, et al. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech. 2010;43(15):3051–3057.
34. Bourke AK, Lyons GM. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys. 2008;30(1):84–90.
35. Bourke AK, O'Donovan KJ, OLaighin GM. Distinguishing falls from normal ADL using vertical velocity profiles. Conf Proc IEEE Eng Med Biol Soc. 2007;3176–3179.
36. Bourke AK, O'Donovan KJ, Nelson J, OLaighin GM. Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system. Conf Proc IEEE Eng Med Biol Soc. 2008;2832–2835.
37. Bourke AK, O'Donovan KJ, Olaighin G. The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls. Med Eng Phys. 2008;30(7):937–946.
38. Lee Y, Lee M. Accelerometer sensor module and fall detection monitoring
system based on wireless sensor network for e-health applications. Telemed J E Health. 2008;14(6):587–592.
39. Lee Y, Kim J, Son M, Lee M. Implementation of accelerometer sensor module and fall detection monitoring
system based on wireless sensor network. Conf Proc IEEE Eng Med Biol Soc. 2007;2315–2318.
40. Zigel Y, Litvak D, Gannot I. A method for automatic fall detection of elderly
people using floor vibrations and sound—proof of concept on human mimicking doll falls. IEEE Trans Biomed Eng. 2009;56(12):2858–2867.
41. Litvak D, Zigel Y, Gannot I. Fall detection of elderly
through floor vibrations and sound. Conf Proc IEEE Eng Med Biol Soc. 2008;4632–4635.
42. Bourennane W, Charlon Y, Bettahar F, Campo E, Esteve D. Homecare monitoring
system: a technical proposal for the safety of the elderly
experimented in an Alzheimer's care unit. IRBM. 2013;34(2):92–100.
43. Charlon Y, Bourennane W, Bettahar F, Campo E. Activity monitoring
system for elderly
in a context of smart home. IRBM. 2013;34(1):60–63.
44. Quagliarella L, Sasanelli N, Belgiovine G. An interactive fall and loss of consciousness detector system. Gait Posture. 2008;28(4):699–702.
45. Quagliarella L, Sasanelli N, Belgiovine G. Performances of an accelerometric device for detecting fall and loss of consciousness. J Appl Biomater Biomech. 2008;6(2):119–126.
46. Lee T, Mihailidis A. An intelligent emergency response system: preliminary development and testing of automated fall detection. J Telemed Telecare. 2005;11(4):194–198.
47. Mihalidis A, Lee T. The role of technology in enhancing safety in the home: detection of fall and emergency situations. http://www.healthplexus.net
. Accessed June 27, 2013.
48. Albert MV, Kording K, Herrmann M, Jayaraman A. Fall classification by machine learning using mobile phones. PLoS One. 2012;7(5):e36556.
49. Aziz O, Robinovitch SN. An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. IEEE Trans Neural Syst Rehabil Eng. 2011;19(6):670–676.
50. Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH. Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans Neural Syst Rehabil Eng. 2010;18(6):619–627.
51. Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Celler BG, Lovell NH. Falls event detection using triaxial accelerometry and barometric pressure measurement. Conf Proc IEEE Eng Med Biol Soc. 2009;6111–6114.
52. Boissy P, Choquette S, Hamel M, Noury N. User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemed J E Health. 2007;13(6):683–693.
53. Bourke AK, van de Ven PW, Chaya AE, OLaighin GM, Nelson J. The design and development of a long-term fall detection system incorporated into a custom vest for the elderly
. Conf Proc IEEE Eng Med Biol Soc. 2008:2836–2839.
54. Boyle J, Karunanithi M. Simulated fall detection via accelerometers. Conf Proc IEEE Eng Med Biol Soc. 2008:1274–1277.
55. Campo E, Grangereau E. Wireless fall sensor with GPS location for monitoring
. Conf Proc IEEE Eng Med Biol Soc. 2008;498–501.
56. Chang SY, Lai CF, Chao HC, Park JH, Huang YM. An environmental-adaptive fall detection system on mobile device. J Med Syst. 2011;35(5):1299–1312.
57. Chao PK, Chan HL, Tang FT, Chen YC, Wong MK. A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration. Physiol Meas. 2009;30(10):1027–1037.
58. de la Guia Solaz M, Bourke A, Conway R, Nelson J, Olaighin G. Real-time low-energy fall detection algorithm with a programmable truncated MAC. Conf Proc IEEE Eng Med Biol Soc. 2010;2423–2426.
59. Diaz A, Prado M, Roa LM, Reina-Tosina J, Sanchez G. Preliminary evaluation of a full-time falling
monitor for the elderly
. In: Proceedings of the 26th Annual International Conference of the IEEE-EMBS; Vol. 1; September 1-5, 2004; San Francisco, CA.
60. Dinh A, Teng D, Chen L, et al. Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention. Conf Proc IEEE Eng Med Biol Soc. 2008;2353–2356.
61. Estudillo-Valderrama MA, Roa LM, Reina-Tosina J, Naranjo-Hernández D. Design and implementation of a distributed fall detection system—personal server. IEEE Trans Inf Technol Biomed. 2009;13(6):874–881.
62. Godfrey A, Bourke AK, Olaighin GM, van de Ven P, Nelson J. Activity classification using a single chest mounted tri-axial accelerometer. Med Eng Phys. 2011;33(9):1127–1135.
63. Huang C-N, Chiang CY, Chen G, Hsu S, Chu W, Chan C. Fall detection system for healthcare quality improvement in residential care facilities. J Med Biol Eng. 2010;30(4):247–252.
64. Hwang JY, Kang JM, Jang YW, Kim H. Development of novel algorithm and real-time monitoring
ambulatory system using Bluetooth module for fall detection in the elderly
. Conf Proc IEEE Eng Med Biol Soc. 2004;3:2204–2207.
65. Kang DW, Choi JS, Lee JW, Chung SC, Park SJ, Tack GR. Real-time elderly
system based on a tri-axial accelerometer. Disabil Rehabil Assist Technol. 2010;5(4):247–253.
66. Kangas M, Konttila A, Winblad I, Jämsä T. Determination of simple thresholds for accelerometry-based parameters for fall detection. Conf Proc IEEE Eng Med Biol Soc. 2007;1367–1370.
67. Kangas M, Konttila A, Lindgren P, Winblad I, Jämsä T. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture. 2008;28(2):285–291.
68. Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring
. IEEE Trans Inf Technol Biomed. 2006;10(1):156–167.
69. Lee RY, Carlisle AJ. Detection of falls using accelerometers and mobile phone technology. Age Ageing. 2011;40(6):690–696.
70. Lindemann U, Hock A, Stuber M, Keck W, Becker C. Evaluation of a fall detector based on accelerometers: a pilot study. Med Biol Eng Comput. 2005;43(5):548–551.
71. Naranjo-Hernandez D, Roa LM, Reina-Tosina J, Estudillo-Valderrama MA. Personalization and adaptation to the medium and context in a fall detection system. IEEE Trans Inf Technol Biomed. 2012;16(2):264–271.
72. Nguyen TT, Cho MC, Lee TS. Automatic fall detection using wearable biomedical signal measurement terminal. Conf Proc IEEE Eng Med Biol Soc. 2009;5203–5206.
73. Niazmand K, Jehle C, D'Angelo LT, Lueth TC. A new washable low-cost garment for everyday fall detection. Conf Proc IEEE Eng Med Biol Soc. 2010;6377–6380.
74. Nocua R, Noury N, Gehin C, Dittmar A, McAdams E. Evaluation of the autonomic nervous system for fall detection. Conf Proc IEEE Eng Med Biol Soc. 2009;3225–3228.
75. Noury N, Barralon P, Virone G, Boissy P, Hamel M, Rumeau P. A smart sensor based on rules and its evaluation in daily routines. In: Proceedings of the 25th Annual International Conference of the IEEE-EMBS; Vol. 4; September 17-21, 2003; Cancun, Mexico.
76. Nyan MN, Tay FE, Tan AW, Seah KH. Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Med Eng Phys. 2006;28(8):842–849.
77. Nyan MN, Tay FE, Murugasu E. A wearable system for pre-impact fall detection. J Biomech. 2008;41(16):3475–3481.
78. Sim SY, Jeon HS, Chung GS, et al. Fall detection algorithm for the elderly
using acceleration sensors on the shoes. Conf Proc IEEE Eng Med Biol Soc. 2011;4935–4938.
79. Tamura T, Yoshimura T, Sekine M, Uchida M, Tanaka O. A wearable airbag to prevent fall injuries. IEEE Trans Inf Technol Biomed. 2009;13(6):910–914.
80. Tolkiehn M, Atallah L, Lo B, Yang GZ. Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Conf Proc IEEE Eng Med Biol Soc. 2011;369–372.
81. Wu G, Xue S. Portable preimpact fall detector with inertial sensors. IEEE Trans Neural Syst Rehabil Eng. 2008;16(2):178–183.
82. Yuwono M, Moulton BD, Su SW, Celler BG, Nguyen HT. Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems. Biomed Eng Online. 2012;11:9.
83. Zhang T, Wang J, Liu P, Hou J. Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. Int J Comput Sci Natl Secur. 2006;6(10):277–284.
84. Auvinet E, Reveret L, St-Arnaud A, Rousseau J, Meunier J. Fall detection using multiple cameras. Conf Proc IEEE Eng Med Biol Soc. 2008;2554–2557.
85. Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J. Fall detection with multiple cameras: an occlusion-resistant method based on 3-D silhouette vertical distribution. IEEE Trans Inf Technol Biomed. 2011;15(2):290–300.
86. Chia-Wen L, Zhi-Hong L. Automatic fall incident detection in compressed video for intelligent homecare. In: Proceedings of the 16th International Conference on Computer on Communications and Networks; August 13-16, 2007; Honolulu, HI.
87. Foroughi H, Aski BS, Pourreza H. Intelligent video surveillance for monitoring
fall detection of elderly
in home environments. In: Proceedings of the 11th International Conference on Computer and Information Technology; December 25-27, 2008; Khulna, Bangladesh.
88. Lee YS, Chung WY. Visual sensor based abnormal event detection with moving shadow removal in home healthcare applications. Sensors (Basel). 2012;12(1):573–584.
89. Leone A, Diraco G, Siciliano P. Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study. Med Eng Phys. 2011;33(6):770–781.
90. Li Y, Ho KC, Popescu M. A microphone array system for automatic fall detection. IEEE Trans Biomed Eng. 2012;59(5):1291–1301.
91. Mirmahboub B, Samavi S, Karimi N, Shirani S. Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans Biomed Eng. 2013;60(2):427–436.
92. Nyan MN, Tay FE, Mah MZ. Application of motion analysis system in pre-impact fall detection. J Biomech. 2008;41(10):2297–2304.
93. Popescu M, Mahnot A. Acoustic fall detection using one-class classifiers. In: Proceedings of the 31st Annual International Conference of the IEEE-EMBS; September 3-6 2009; Minneapolis, MN.
94. Rimminen H, Lindström J, Linnavuo M, Sepponen R. Detection of falls among the elderly
by a floor sensor using the electric near field. IEEE Trans Inf Technol Biomed. 2010;14(6):1475–1476.
95. Rougier C, Meunier J, St-Arnaud A, Rousseau J. Monocular 3D head tracking to detect falls of elderly
people. In: Proceedings of the 28th Annual International Conference of the IEEE-EMBS; August 30–September 3, 2006; New York City, NY.
96. Rougier C, Meunier J, St-Arnaud A, Rousseau J. Fall detection from human shape and motion history using video surveillance. In: Proceedings 21st International Conference on Advanced Information Networking and Applications Workshop; Vol. 2; May 21-23, 2007; Niagara Falls, Canada.
97. Shieh WY, Huang JC. Falling
-incident detection and throughput enhancement in a multi-camera video-surveillance system. Med Eng Phys. 2012;34(7):954–963.
98. Ariani A, Redmond SJ, Chang D, Lovell NH. Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors. Conf Proc IEEE Eng Med Biol Soc. 2010;2115–2118.
99. Ariani A, Redmond SJ, Chang D, Lovell NH. Simulated unobtrusive falls detection with multiple persons. IEEE Trans Biomed Eng. 2012;59(11):3185–3196.
100. Bloch F, Gautier V, Noury N, et al. Evaluation under real-life conditions of a stand-alone fall detector for the elderly
subjects. Ann Phys Rehabil Med. 2011;54(6):391–398.
101. Doukas CN, Maglogiannis I. Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Trans Inf Technol Biomed. 2011;15(2):277–289.
102. Gietzelt M, Spehr J, Ehmen Y, et al. GAL@Home: a feasibility study of sensor-based in-home fall detection. Z Gerontol Geriatr. 2012;45(8):716–721.
103. Srinivasan S, Han J, Lal D, Gacic A. Towards automatic detection of falls using wireless sensors. Conf Proc IEEE Eng Med Biol Soc. 2007;1379–1382.
104. Tasoulis SK, Doukas CN, Maglogiannis I, Plagianakos VP. Statistical data mining of streaming motion data for fall detection in assistive environments. Conf Proc IEEE Eng Med Biol Soc. 2011;3720–3723.
105. Zhang Z, Kapoor U, Narayanan M, Lovell NH, Redmond SJ. Design of an unobtrusive wireless sensor network for nighttime falls detection. Conf Proc IEEE Eng Med Biol Soc. 2011;5275–5278.
106. Prado-Velasco M, del Rio-Cidoncha MG, Ortiz-Marin R. The inescapable smart impact detection system (ISIS): an ubiquitous and personalized fall detector based on a distributed “divide and conquer strategy.” Conf Proc IEEE Eng Med Biol Soc. 2008;3332–3335.
107. Doughty K, Lewis R, McIntosh A. The design of a practical and reliable fall detector for community and institutional telecare. J Telemed Telecare. 2000;6(suppl 1):S150–S154.
108. Otto CA, Chen X. Automated fall detection: saving senior lives one fall at a time. Caring. 2009;28(3):44–46.
109. Narayanan MR, Lord SR, Budge MM, Celler BG, Lovell NH. Falls management: detection and prevention, using a waist-mounted triaxial accelerometer. Conf Proc IEEE Eng Med Biol Soc. 2007;4037–4040.
110. Depursinge Y, Krauss J, El-khoury MS. Inventors; device for monitoring
the activity of a person and/or detecting a fall, in particular with a view to providing help in the event of an incident hazardous to life or limb. United States Patent US6201476 B1, March 13, 2001.
111. Prado M, Reina-Tosina J, Roa L. Distributed intelligent architecture for falling
detection and physical activity analysis in the elderly
. In: Proceedings of the 24th Annual International Conference of the IEEE-EMBS; Vol. 3; October 23-26, 2002; Houston, TX.
112. Bourke AK, O'Donovan K, Clifford A, ÓLaighin G, Nelson J. Optimum gravity vector and vertical acceleration estimation using a tri-axial accelerometer for falls and normal activities. Conf Proc IEEE Eng Med Biol Soc. 2011;7896–7899.
113. Mathie MJ, Basilakis J, Celler BG. A system for monitoring
posture and physical activity using accelerometers. In: Proceedings of the 23th Annual International Conference of the IEEE-EMBS; Vol. 4; October 23-26, 2001; Istanbul, Turkey.
114. Tamura T, Yoshimura T, Horiuchi F, Higashi Y, Fujimoto T. An ambulatory fall monitor for the elderly
. In: Proceedings of the 22nd International Conference on Engineering in Medicine and Biology Society; Vol. 4; July 23-28, 2000; Chicago, IL.
115. Van Wieringen M, Eklund J. Real-time signal processing of accelerometer data for wearable medical patient monitoring
devices. Conf Proc IEEE Eng Med Biol Soc. 2008;2397–2400.
116. Tamura T, Yoshimura T, Sekine M. A preliminary study to demonstrate the use of an air bag device to prevent fall-related injuries. Conf Proc IEEE Eng Med Biol Soc. 2007;3833–3835.
117. Fernandez-Luque FJ, Zapata J, Ruiz R. A system for ubiquitous fall monitoring
at home via a wireless sensor network. Conf Proc IEEE Eng Med Biol Soc. 2010;2246–2249.
118. Noury N, Herve T, Rialle V, et al. Monitoring
behavior in home using a smart fall sensor and position sensors. In: Proceedings 1st International Conference on Microtechnologies in Medicine and Biology; October 12-14, 2000; Lyon, France.
119. Villacorta JJ, Jiménez MI, Del Val L, Izquierdo A. A configurable sensor network applied to ambient assisted living. Sensors (Basel). 2011;11(11):10724–10737.
120. Wu G. Distinguishing fall activities from normal activities by velocity characteristics. J Biomech. 2000;33(11):1497–1500.
121. Nait-Charif H, McKenna SJ. Activity summarisation and fall detection in a supportive home environment. In: Proceedings 17th International Conference on Pattern Recognition; Vol. 4; August 23-26, 2004; Cambridge, England, UK.
122. Anderson D, Keller JM, Skubic M, Xi C, Zhihai H. Recognizing falls from silhouettes. In: Proceedings of the 28th Annual International Conference of the IEEE-EMBS; August 30–September 3, 2006; New York City, NY.
123. Spher J, Gvercin M, Winkelbach S, Steinhagen-Thiessne E, Wahl F. Visual fall detection in home environments. In: Proceedings 6th International Conference of the International Society of Gerontechnology; Vol 7, No. 2; June 4-5, 2008; Pisa, Italy.
124. Della Toffola L, Patel S, Chen BR, Ozsecen YM, Puiatti A, Bonato P. Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5331–5334.
125. Cucchiara R, Prati A, Vezzanie R. A multi-camera vision system for fall detection and alarm generation. Expert Syst. 2007;24(5):334–345.
126. Sposaro F, Tyson G. iFall: an Android application for fall monitoring
and response. Conf Proc IEEE Eng Med Biol Soc. 2009;6119–6122.
127. Mellone S, Tacconi C, Schwickert L, Klenk J, Becker C, Chiari L. Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Z Gerontol Geriatr. 2012;45(8):722–727.
128. Kangas M, Vikman I, Nyberg L, Korpelainen R, Lindblom J, Jämsä T. Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture. 2012;35(3):500–505.
129. Patel PA, Gunnarsson C. A passive monitoring
system in assisted living facilities: 12-month comparative study. Phys Occup Ther Geriatr. 2012;30(1):45–52.
130. Gövercin M, Költzsch Y, Meis M, et al. Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inform Health Soc Care. 2010;35(3/4):177–187.
131. Noury N, Galay A, Pasquier J, Ballussaud M. Preliminary investigation into the use of autonomous fall detectors. Conf Proc IEEE Eng Med Biol Soc. 2008;2828–2831.
132. Aud MA, Abbott CC, Tyrer HW, et al. Smart Carpet: developing a sensor system to detect falls and summon assistance. J Gerontol Nurs. 2010;36(7):8–12.
133. Horton K. Falls in older people: the place of telemonitoring in rehabilitation. J Rehabil Res Dev. 2008;45(8):1183–1194.
134. Londei ST, Rousseau J, Ducharme F, et al. An intelligent videomonitoring system for fall detection at home: perceptions of elderly
people. J Telemed Telecare. 2009;15(8):383–390.
135. Marquis-Faulkes F, McKenna SJ, Newell AF, Gregor P. Gathering the requirements for a fall monitor using drama and video with older people. Technol Disabil. 2005;17(4):227–236.
136. Brownsell S, Hawley M. Fall detectors: do they work or reduce the fear of falling
? Housing Care Support. 2004;7(1):18–24.
137. Brownsell SJ, Bradley DA, Bragg R, Catlin P, Carlier J. Do community alarm users want telecare? J Telemed Telecare. 2000;6(4):199–204.
138. Klenk J, Becker C, Lieken F, et al. Comparison of acceleration signals of simulated and real-world backward falls. Med Eng Phys. 2011;33(3):368–373.
139. Bagalà F, Becker C, Cappello A, et al. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS One. 2012;7(5):e37062.
140. Wilding MJ, Seegert L, Rupcic S, Griffin M, Kachnowski S, Parasuraman S. Falling
short: recruiting elderly
individuals for a fall study. Ageing Res Rev. 2013;12(2):552–560.
Example Search Strategy for PubMed
- 1. “Monitoring, Ambulatory” [Mesh]) AND “Accidental Falls” [Mesh]
- 2. “Accidental Falls” [majr]) AND (“Monitoring, Ambulatory” [Mesh] OR “instrumentation” [Subheading] OR “Clinical Alarms” [Mesh])
- 3. (“Accidental Falls” [majr]) AND (“Monitoring, Physiologic” [Mesh] OR “instrumentation” [Subheading] OR “Clinical Alarms” [Mesh])
- English [Language]