Article Content

INTRODUCTION

Adults aged 65 years or older experience higher rates of falling and are generally at a higher risk for falls.1-4 One in every 3 persons older than 65 years is estimated to fall 1 or more times each year.4-6 Falls and fall-related injuries represent a significant threat to the health and independence of adults aged 65 years and older. Falls can have severe consequences such as injury or death; in 2010 in the United States, 21,649 older adults died from fall-related injuries.7 Even if a fall does not result in a physical injury, it can often produce fear of falling, resulting in a decrease in mobility, participation in activities, and independence.8,9 Fear of falling can be amplified in the presence of the "long lie," which is identified as involuntarily remaining on the ground for an hour or more following a fall.1 Such an event can result in substantial damage to the individual's body and morale. Lying on the floor for an extended period of time often results in several medical complications such as dehydration, internal bleeding, pressure sores, rhabdomyolysis, or even death. Half of those who experience the "long lie" die within 6 months of the fall.10 A recent cohort study reported that a "long lie" was seen in 30% of participants with history of falls;11 therefore, it represents a great threat to the long-term health of older adults.

 

Evidence-based methods to prevent falls include regular exercise, vitamin D supplementation, and having regular fall risk assessments.2,12-14 However, despite prevention efforts, falls are still likely to occur as one ages, and they need to be quickly identified to prevent further injury to the fallen individual. Personal emergency response systems (PERS) represent one commercial solution to addressing this issue. These clinical alarm systems provide a way for individuals who fall to contact an emergency center by pressing a button.15 While appropriate in many situations, the PERS system is rendered useless in the event that the person is unconscious or unable to reach the button. Even when the system is available, a recent cohort study found that around 80% of older adults wearing a PERS did not use their alarm system to call for help after experiencing a fall.11

 

Because of these challenges associated with PERS systems, passive monitoring solutions have been proposed to more accurately detect falls. Several solutions are currently available with most devices being worn by a person (eg, as a wristwatch or attached to clothing). Other solutions include technologies embedded in the residential setting such as cameras, microphones, or pressure sensors installed underneath the flooring. Previous fall-detection literature reviews have dealt with the principles of fall detection, the ethical issues associated with these systems, or the practicality of such systems.16-19 However, with the wide variety and sheer number of available systems, there is a need to synthesize the evidence of their ability to accurately detect falls.

 

Fall-detection technologies enable rapid detection and intervention for individuals who have experienced a fall. This ability could reduce the physical and mental damage caused not only by the fall but time after a fall before discovery. These technologies will help reassure those at a risk of falling as well as their caregivers and family. In the future, these devices can help physical therapists and other clinicians clearly understand not only when the person experienced the fall but also circumstances surrounding the fall, allowing for better treatment of the individual in question.

 

The primary aim of this article was to review the evidence on fall-detection devices and to analyze their level of success in automatically detecting falls. Secondary aims of this review were to examine older adults' usage and perceptions of these devices as well as the implementation of these devices in "real world" situations. "Real world," as we define it for the purposes of this review, is a certain period of time in which subjects use the device in their normal environment without any instructions given by the researcher. Simulating falls or activities of daily living, as instructed by the researcher, in one's home would not be viewed as a "real world" situation for purposes of this review.

 

METHODS

The systematic literature review was conducted in PubMed, CINAHL, EMBASE, and PsycINFO from their respective inception dates to June 25, 2013. See the Appendix for detailed search strategy used for one of the databases.

 

We included articles in this review if they discussed a project or multiple projects involving a system with the purpose of detecting when an adult has fallen (including studies ultimately designed for use with adults but with laboratory tested "subjects," ie, dummies simulations, actors). While we examined systems designed for adults, it was not a requirement for inclusion in this review that the system specifically target adults older than 65 years. However, we did exclude systems that targeted children due to differences in fall patterns between children and adults. We excluded articles if they were literature reviews or if they looked at fall risk, fall detection in children, fall prevention, or a PERS device.

 

The criteria for inclusion or exclusion were finalized by the team, and the primary search was carried out by the first author (S.C). Article selection was conducted by the first author who reviewed full texts of the relevant articles using a data extraction spreadsheet developed for this review. To ensure reliability of article selection, 2 of the authors (G.D. and H.T.) blindly and independently assessed a subset of articles from the initial search for the appropriateness of inclusion in the final review. There was full agreement between all authors on articles selected for inclusion.

 

Quality scoring was conducted using the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI).20 To account for the variety of manuscripts, a condensed version of the STARE-HI was used, which included 3 items deemed most important in the mini-STARE-HI21,22 as well as 3 additional criteria: (1) description of how the system works, (2) baseline demographic data/characteristics of participants, and (3) basic outcome numbers (eg, number of fall events, types of events). If the manuscript did address the criterion, they were given a score of 1, and if they did not, they were given a score of 0. Thus, the possible range of quality score is 0 to 6, with a 6 indicating the paper addressed all of the STARE-HI quality criteria. To ensure reliability of quality scoring, one of the authors (H.T.) blindly and independently scored a random subset of articles. Differences in scoring were discussed and corrected before a final round of scoring was conducted.

 

The initial search yielded 617 results from which all abstracts were read to further determine eligibility for this review. Five hundred sixteen papers found in the initial search did not focus on fall detection but instead focused on various topics from gait, balance, and posture to seizures and medical instrumentation. These papers were eliminated leaving a total of 101 unique papers to be read in full. Scanning the reference lists of these papers allowed for the identification of 24 more papers that dealt primarily with fall detection, for a total of 125 papers. In reading the full texts, 12 dealt with children, fall risk, fall prevention, or a PERS device and were excluded from this review. Of the remaining 113 papers, 31 did not attempt to evaluate their system based on accuracy, sensitivity, or specificity of a detection device. Figure 1 fully diagrams the literature identification and screening process.

  

Figure 1. Flow Diagram of the literature review.

RESULTS

The "Results" section is divided into 3 parts. It first provides an overview of currently available systems and their classifications. Then, for ease of comparison, the next 2 sections are divided into projects that used measures of sensitivity, specificity, or accuracy to evaluate their device and projects that used other methods to evaluate the device.

 

Current State of Fall-Detection Systems

The various existing detection devices can be divided into wearable and nonwearable systems. Wearable systems generally consist of placing an accelerometer upon the subject which can detect changes in acceleration, planes of motion, or impact in order to detect falls.23-25 Nonwearable systems include cameras,26-28 acoustic sensors,29,30 and pressure sensors31 that are placed in the subject's normal environment and use various measurements to determine if the subject has fallen. From this review, we identified 57 projects using wearable systems and 35 projects involving nonwearable systems (regardless of evaluation technique and not including projects using multiple systems).

 

Projects Evaluating the Device Based on Accuracy, Sensitivity, or Specificity

Eighty-two papers described some method of device testing that included sensitivity, specificity, or accuracy. These were further categorized by the different kind of sensors they were describing. Some papers described the results and procedures resulting from the same project.23,32-47 For the purpose of this analysis, we took their findings into account only once, resulting in 74 total projects.

 

Forty-two of these projects discussed the use of wearable sensors. Nonwearable devices included 16 projects using cameras or motion sensors, 4 projects using microphones, and 2 projects that used a floor sensor. There were also 10 projects that used multiple sensor systems to detect whether a person had fallen. Multiple sensors, as we have defined them, can be any combination of 2 or more sensor types used to monitor a subject. Tables 1 through 3 list specific details about the various projects including how the researchers defined their subjects and their stated values for accuracy, sensitivity, or specificity. Medians of accuracy, sensitivity, and specificity are presented throughout the following sections. Some were difficult to determine as many projects either did not provide a value or provided a range of values depending on the number of tests conducted for various types of falls (falling forward, falling backward, etc). The medians presented are taken only from papers that provided a single overall value for each element (ie, papers using ranges or declaring multiple values for each fall types were not included in the calculation of a median). This does not account for many variables including year of the project or testing procedure and thus should not be used to compare the success of different device types and are meant only to provide a high-level view of how each type of device performs.

  

Table 1-a. Summary of Projects Involving Wearable Systems

 

Table 2-a. Summary of Projects Involving Nonwearable Systems

 

Table 3-a. Summary of Projects Involving Multiple Devices

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

  

Table 1-b. Summary of Projects Involving Wearable Systems

 

Table 1-c. Summary of Projects Involving Wearable Systems

 

Table 1-d. Summary of Projects Involving Wearable Systems

 

Table 1-e. Summary of Projects Involving Wearable Systems

 

Table 1-f. Summary of Projects Involving Wearable Systems

 

Table 2-b. Summary of Projects Involving Nonwearable Systems

 

Table 2-c. Summary of Projects Involving Nonwearable Systems

 

Table 3-b. Summary of Projects Involving Multiple Devices

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.

 

Multiple sensor projects used various combinations of systems to detect a fall (Table 3). Papers that compared their systems with another system were not included in this category. Most of these projects were fairly recent and were implemented with the goal of more accurately measuring a fall by evaluating multiple signals. These projects had a surprisingly small number of human participants with some using computer-generated falls or using anthropomorphic dummies for falls. However, 3 more recent projects have been tested with older adults in real-world environments: a single study completed within their homes102 and 2 in a clinic setting.43,100

 

Table 4 provides a high-level comparison between the different types of devices. The average number of subjects and the types of subjects involved were taken only from papers that clearly defined their samples and excluded any simulated data or fall dummies. As with earlier medians and ranges, these numbers should be interpreted cautiously as they do not account for many variables in the evaluation process including the number of trials, the number of subjects, types of falls, etc.

  

Table 4. Summary of Project Sampling Characteristics and Fall Device Performance by Device Type

Projects Evaluating Their Device in Other Ways

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.

 

DISCUSSION/CONCLUSION

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.

 

LIMITATIONS

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.

 

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Appendix

Example Search Strategy for PubMed

1. "Monitoring, Ambulatory" [Mesh]) AND "Accidental Falls" [Mesh]

Or

2. "Accidental Falls" [majr]) AND ("Monitoring, Ambulatory" [Mesh] OR "instrumentation" [Subheading] OR "Clinical Alarms" [Mesh])

Or

3. ("Accidental Falls" [majr]) AND ("Monitoring, Physiologic" [Mesh] OR "instrumentation" [Subheading] OR "Clinical Alarms" [Mesh])

AND

English [Language]

[Context Link]

elderly; falling; monitoring