Mobility may be an important factor for fear of falling due to significantly high correlations [1
]. Falling refers to the unexpected change in body position when the center of gravity is out of balance. The body is unable to respond effectively, and falls onto the floor or a lower place [2
]. This issue has attracted an increased amount of attention as society ages. Aging degrades lower limb function and reduces the ability of the elderly to perform daily activities, even leading to increased probability of falls [3
]. According to the study by [4
], falling is the major cause of accidental death for people aged 65 and above. The body depends on the balancing mechanism to prevent falls related to inertial force of and on the body. For elderly, there is a gap between the physical response and self-expectation; self-awareness for falls is also low. Risk assessment and preventive measures for falls in the elderly will become an important area for developing care of aged populations [5
]. Falling, besides causing death, may also induce disability and injuries to some degree [4
]. The study of mobility of elderly [6
] indicated that many falls have considerable relationships with movement disorder of elderly people (e.g., assess by standing up from the chair and walking ability). When summarizing fall risk factors, Rubenstein et al. [8
] also suggested there is a strong relationship between falls and decreased muscle strength, followed by unsteady gait and balance disorder, which shows that declined mobility is the main cause of elderly people’s falls.
The causes of falls are quite complex and include internal and external factors. For example, degradation of physical function is caused by aging, acute and chronic illnesses, drugs, and safety of the home environment, which are all related to fall occurrence. Typically, falls in the elderly are not caused by a single but rather many factors. Assessment and intervention from multiple aspects are required to effectively prevent the occurrence of falls and other related injuries [9
]. To identify persons at risk of falling, thus being eligible for preventive treatment, many risk assessment tools, e.g., the 3-minute Timed Up and Go test (TUG) [10
] or the Short Form Berg Balance Scale (SFBBS) [11
] have been developed and evaluated in a multitude of studies. However, when medical professionals are performing assessments, if they not only use their professional knowledge to do the evaluation, but also include objective devices, this may allow them to include information such as user environment and user time. Evaluation can be performed and not limited to hospitals.
In addition to the above-mentioned clinical tests, wearable accelerometers are a viable technology for fall risk assessment, joining clinical and laboratory methods as acceptable assessment tools. Inertial-sensor-based systems have the benefits of portability, low cost, and few constraints on the types of movements that can be monitored [12
]. Therefore, studies using wearable accelerometers can collect motion data for fall risk assessments. Tamura [13
] designed a body worn accelerometer for fall detection suggesting falls occur at angles of inclination greater than 60 degrees. Kulkarni and Basu [14
] discussed wearable tri-axial accelerometers based on fall detectors by presenting their types, mountings, and methods of detecting falls to minimize risk of injury. Marschollek et al. [15
] developed an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. Geriatric inpatients wore an accelerometer on the waist during a Timed Up and Go test and a 20 m walk; here, the wearable accelerometer was also used to monitor the physiological status of elderly in daily life [16
]. Timed Up and Go (TUG) times have been associated with impaired mobility and an increased risk of falling [17
]. TUG is an excellent indicator of clinically testing the walking ability of patients with musculoskeletal neurological system injury [10
]. Accelerometers were the only inertial sensor in 70% of the studies, whereas gyroscopes were the only inertial sensor in one study [18
]. Previous studies showed that tri-axial accelerometers are more accurate than other types of accelerometers [10
]. We hope to use accelerometers to objectively record TUG in addition to clinical tests, thus providing multiple indicators of fall risk assessment. It is worth mentioning the clinical tests have a clear score boundary, as the distinction among and analyses and research behind accelerometers mostly reveal the factors or risks of falling using experiments. From a literature review, Howcroft et al. [12
] suggested three main methods for classifying subjects into faller and non-faller categories: retrospective fall history (30%), prospective fall occurrence (15%), and scores on clinical assessments (32.5%). Horak et al. [24
] suggested, to be useful for clinicians, objective measures of balance and gait need to be available outside the laboratory, where recent advances in body-worn sensors have made this portability possible. Laboratory tests of gait and balance involve expensive, highly technical, non-portable equipment, such as video-based motion analysis systems and force plates, which are not practical for clinical environments or for multisite clinical trials [24
]. Falls often occur during everyday life. In previous studies, people used well-controlled conditions to perform experiment investigation [10
]. However, in real life, we cannot eliminate variables. We try to use an objective method to collect data and then perform indicative discussion. In this study, we try to discuss with respect to community services for the elderly in real world.
Previous studies discussing the feature of body-fixed accelerometers can provide insights into TUG performance [15
]. Features included Sit-to-Stand, Stand-to-Sit durations, amplitude range (Range) and slopes (Jerk), mean step duration, step length, and number of steps during the TUG test. Acceleration median and standard deviation (SD) were also calculated. Further, Pincus et al. [28
] used Approximate Entropy (ApEn) computed for accelerometer sensors. Entropy measures for time series, such as sample entropy (SampEn) and approximated entropy (ApEn) [29
], do indeed measure the unpredictability (opposite of regularity) of a time series. More recently, Costa et al. [30
] have proposed a new entropy-based measure for time series that seems to better quantify complexity, coining it Multiscale Entropy (MSE). Tsai [32
] used MSE to measure the accelerometer, considering that multiscale entropy curves can be used to compare the differences between different statuses, i.e., when the body is under a relatively unbalanced status, the multiscale entropy curve will decrease. MSE can be used to quantify complexity on widely varying timescales; it is also worthwhile to explicitly compare the results of MSE used for time series analysis with classical characterizations of scaling and self-similarity. Signals with a higher level of complexity have greater self-similarity. In the context of biomedicine, greater physiological complexity indicates greater adaptability to the external environment; the reverse also holds true. This method is commonly used in the study of physiological signals and pathology [31
]. In this study, due to the wearable accelerometer measure tri-axial signal being too messy [32
], MSE is derived from the Approximate Entropy (ApEn) used by Pincus et al. [28
]. In the past, Pincus et al. computed for sensors. Therefore, in addition to the analysis method using the accelerometer as described above, we try to use data from MSE analysis to quantify the balance of the body.
This study combines accelerometer sensing technology and clinical tests using MSE to analyze posture control ability to further discuss indicators of fall risk assessment. We also discuss what features of accelerometers outside the lab, e.g., within a community services context, can categorize fallers. The aim of this paper is to examine the MSE and sensor-based methods for fall risk assessment against conventional and established methods.
3. Results and Discussion
Our discussion and analysis can be divided into three main parts. (a) We simply use a clinical test to determine who has fall risk in order to categories the people with and without a fall risk (this includes efficiency); (b) We use the results in (a) to compare accelerometer features, and then use t
-test analysis to verify the categorization of fall risk; (c) Finally, we use the results from (a) to compare the MSE results, and we then use t-test analysis to verify the categorization of fall risk. Since scales of clinical tests have clear boundary scores, Karthikeyan et al. [42
], Kim et al. [43
], İlçin et al. [44
] and Li et al. [45
] proposed that balance is considered as impaired when the score is 23 or below for BBS. Barry et al. [37
], Shumway-Cook et al. [46
], Lindsay et al. [47
] and Kwoka et al. [48
] recommend that it is considered a high risk if the time for TUG is greater than 13.5 s. Furthermore, in clinical judgments of the SPMSQ, there may be a risk of dementia if a subject answered three or more questions incorrectly in the test. We try to use these indicators as judgment criteria for categorizing fallers and non-fallers. Table 3
shows the results of the fall risk assessment tests for predicting fall events from a community service context.
We then used the results from the clinical indicators to compare the features extracted from the accelerometer (Section 2.5
) and distinguished elderly people with fall risk using statistics from the significance of the TUG test result. Since previous publications have performed discussion and verification on the clinical test score (according to fallers and non-fallers), we try to divide each clinical test into two groups. When comparing accelerometer features with the t-test, a p
-value smaller than 0.05 implies a statistically significant difference, indicating fallers distinguished by features are quite similar to the clinical test result. For example, in Table 4
, two groups of people from TUG are assigned an overall steps feature, and the t-test was used for verification. The p
-value is 0.044, which shows significant difference. Thus, this feature is able to distinguish people with fall risk. As indicated by Table 2
, in terms of TUG, the more defining features are steps, average step length, test time, Slope(A), time(A) and slope(B) in Sit(A)-to-Stand(B), and range(A), slope(B), and time(B) in Stand(B)-to-Sit(A). Regarding BBS, the more defining features are test time, Slope(A), time(A) and slope(B) in Sit(A)-to-Stand(B), and range(A) and slope(B) in Stand(B)-to-Sit(A). Regarding the SPMSQ, only range(A) in Stand(B)-to-Sit(A) are defining features. If we look at the screening results of both TUG and BBS, the defining features are test time, slope(A) and slope(B) in Sit(A)-to-Stand(B), and range(A) and slope(B) in Stand(B)-to-Sit(A). Looking at the screening results of TUG and SPMSQ, only range(A) in Stand(B)-to-Sit(A) is a defining feature, which is the same result for the SPMSQ. These results suggest that slope(B) in Stand-to-Sit is the most defining feature. Fallers can be effectively categorized irrespective of the critical value used in the clinical test. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and combined TUG and BSS are all distinguishable, showing MSE can effectively classify participants in these clinical tests using behavioral actions.
In general, distinguishing global features does not appear as effective as Sit(A)-to-Stand(B) and Stand(B)-to-Sit(A). As shown in the TUG task, compared to the sit and stand motion, walking is less effective in determining fall risks, which corroborates with the findings of Weiss et al. [17
]—relevant and useful information lies in the acceleration signal of the TUG, especially in the Sit-to-Stand and Stand-to-Sit intervals. In general, distinguishing slope and range is more effective, which accords with the previous discussion of fallers and non-fallers from acceleration-derived measures by the TUG test that differed in the two groups [17
]; among these studies, slope and range are the features showing differences between the two groups. It appears that these TUG features are common among diverse groups of fallers. To the best of our knowledge, we report here for the first time on the application of accelerometer-based measures that systematically evaluate TUG performance in a community service context, focusing on the Sit-to-Stand and Stand-to-Sit transitions. Looking at the screening results of BBS and SPMSQ, only range(A) in Stand(B)-to-Sit(A) is a defining feature.
Further, we also used MSE for comparisons and analyses. As shown in Table 5
, we found by categorizing fallers and non-fallers by critical values from clinical tests using MSE. MSE has previously been used to detect the complexity of physiological signals, such as heart beat [30
] brain waves [53
], acceleration [32
], and postural stability [52
]. The results all showed that MSE can effectively identify objective data. The results of this study, compared with the subjective SPMSQ survey, show MSE is most effective under tasks with behavioral actions (TUG, BBS). In addition, past studies using MSE analyzed postural stability mostly with tasks under experimental environments, such as opening eyes with both feet planted vs. opening eyes with a single foot planted [55
], quiet standing vs. dual tasking [56
], or subject type, such as elderly vs. young man (or patient vs. non-patient) [57
]. Therefore, the tasks were used to compare the difference in complexity level. However, we used MSE in this study to analyze data collected in a non-controlled community service environment, but there was no difference in test type and experimental task. The results show that it can also effectively distinguish fallers and non-fallers, indicating that the acceleration collected by MSE using the accelerometer is also an effective defining feature.
We can conclude by our preliminary results that recording TUG using a wearable accelerometer and simultaneously providing quantifiable analysis and more effective defining features in real life provides objective clinical reference data. As mentioned by Weiss et al. [17
], compared to the traditional method of using a stopwatch to distinguish fallers from non-fallers, we obtained significant results using TUG durations derived from the acceleration signal. To the best of our knowledge, we report for the first time using MSE for applying accelerometer-based measures that systematically evaluate TUG performance in a community service context compared with other clinical tests. As this is an inaugural study using MSE analysis with accelerometers, we attempt to prove that this analysis method is effective in categorizing fallers and non-fallers.