Automated detection of near falls: algorithm development and preliminary results

Falls are a significant cause of morbidity and mortality, especially among older adults and many patient populations [1]. In June of 2009, research on falls among the elderly was listed as the 3^rd item in the top priority group in the Institute of Medicine's report to Congress on national priorities for the United States [2]. Much effort has been devoted to the development of methods for evaluating fall risk [3–6], but the most common means of quantifying falls remains self-report. Despite its widespread use, this method has three key limitations: 1) it is subjective in nature, relying on subjects' motivation and memory (which can be problematic in its own right), 2) it requires a long observation period (e.g., typically six months or more), and 3) sensitivity may be lacking (e.g., subjects are typically classified as fallers and non-fallers, but the absence of a more refined scale may limit sensitivity and the ability to evaluate intervention efficacy).

Different techniques have been developed to automatically identify falls and related measures with varying degrees of success using sensors embedded in the home environment or body-fixed sensors [7–9]. Body-worn fall-detection systems are intended for long-term, automated detection of activity, in general, and falls, more specifically [5, 10–16]. For example, methods based on accelerometry have been proposed as being suitable for the detection of falls in ambulatory subjects [5, 7, 14, 17–19]. These methods have been developed as part of alarm and similar warning systems in order to automatically identify an older adult who has experienced a fall and is in need of immediate assistance. This approach is appropriate for alarm and related applications and has, indeed, been used successfully for these purposes and for the assessment of activity. It is, nonetheless, important to keep in mind that a community-living older adult typically falls less than two times per year [20] and that falls, at least among the healthy elderly, are relatively rare events, albeit with very significant consequences. Thus, continuous monitoring of falls using body-fixed sensors would generally require very long periods of observation (e.g., half a year) to capture a fall incident, minimizing the practicality and feasibility of using this approach to quantify fall risk by measuring actual falls.

We hypothesized that methods based on body-fixed sensors could, however, be adapted to identify missteps or near falls, potentially enhancing the utility of an approach for assessing fall risk. Missteps and near falls are used here synonymously as a stumble or loss of balance that would result in a fall if sufficient recovery mechanisms were not activated [21]. Automatic identification of near falls should, a priori, provide objective quantification of a sensitive marker of fall risk, perhaps over a shorter observation time periods. Indeed, a number of studies have found that near falls based on self-report are related to fall risk [21–27], that near falls are more frequent than falls [21–23, 25, 27, 28], and that near falls may occur before falls [23–25], enhancing the potential predictive value of near falls. These properties indicate that near falls are clinically relevant markers of falls worthy of further study. Objective techniques for quantifying these events are, however, lacking [21]. The primary aim of the present study was to begin to develop and assess signal processing methods for detecting nearfalls using body-fixed sensors.

The results of the present study demonstrate that a single accelerometer may be placed on the trunk of an individual to automatically distinguish near falls from other stepping patterns, with reasonable sensitivity and specificity. Interim analysis of a follow-up study among elderly fallers and non-fallers who walked over-ground also support the idea that these objectively identified near falls are more common among older adults with a history of falling, consistent with the results of self-report studies of near falls [21–28]. Taken together, these findings suggest that perhaps long-term recordings and measurement of near falls, as subjects carry out activities of daily living, is likely to be a clinically relevant, objective adjunct measure of fall risk, possibly improving sensitivity and reducing the observation time required.

This preliminary study has several limitations. Ongoing studies are designed to examine how the developed algorithms work in real-world conditions and to evaluate the predictive value of acceleration-derived measures of near falls in different control and patient populations (e.g., patients with neurodegenerative disease) and in aging (e.g., young vs. older adults). Normal balance responses may have been altered and restricted by the treadmill setup (e.g., the treadmill provides bars for support and promotes continuous walking). In this initial study, we focused on the identification of near falls, but made no attempt at differentiating between the loss of balance and the recovery. Theoretically, these are two distinct processes. In this work, we aimed to identify the "stumble" or near fall. It is, however, possible that some of the recovery phase may have been identified, although most of the derived parameters that had good success in identifying these events are, at least intuitively, more likely related to the loss of balance than to the recovery process. Further work is also needed to verify that the developed algorithms are successful at identifying near falls in normal and free walking environments. There are some subtle differences between over-ground walking and treadmill walking and, as a result, the accelerometer signal is not identical in both conditions. However, even on a treadmill, the signals from all three axes have a form that is similar to over-ground walking, supporting the idea that over-ground near falls can also be detected using the algorithms described.

Despite these and other limitations, the initial results reported here motivate continued work along these lines, provide a basis for future studies in both in the lab and at-home settings, and suggest that a tri-axial accelerometer can successfully identify near falls and may also have the potential for improving the objective evaluation of fall risk.