General reviewA proposal for the classification and evaluation of fall detectorsUne proposition pour la classification et l’évaluation des détecteurs de chutes
Introduction
Falls affect each year one in two people over 80 years of age. It thus concerns tens of million of elderly people throughout the world; approximately 100 million people in developed countries. In this segment of the population, a fall may have lethal consequences. It also causes many disabling fractures [1] and dramatic psychological consequences, which reduces the independence of the person [2], [3], [4]. Falls in the elderly is thus a major public health problem.
The fall consequently raises the interest of searchers, and particularly the “early” detection of the fall. Indeed, as most the elderly fallers cannot return to a standing position on their own following a fall [5], a relationship was established between the delay before intervention and the morbidity–mortality rate [6], [7]. It is therefore important to respond very quickly to a fall.
The detection of the fall is also an interesting scientific problem because it is an ill-defined process and can thus be approached using various methods.
The goals of this study were to classify the various approaches to detect the fall and to point out the difficulty of comparing the results of these studies, thus the need for a common evaluation benchmark is evident. This article starts with a definition of the fall of the elderly, a discussion on the physics of a fall and its detection, and reviews the main academic and industrial research in this developing field. The authors then suggest a classification of the various approaches. In the last part, a set of evaluation parameters and a proposal of a common evaluation framework for fall detection systems are presented.
Section snippets
Definition of a fall detection
Everybody has experienced an unwanted fall, whether in childhood while training to walk, or occasionally in adulthood. The fall mechanism is thus well-known to everybody. To face the fall, corrective and protective mechanisms were developed; athletes can even control “high energy” falls. Nevertheless, it is difficult to describe precisely the phenomenon, and even harder to imagine the means for its detection.
Obviously, the fall of a person can be described as the rapid change from the
Material and methods for fall detection
The fall may be broken down into four phases (Fig. 1), that is, the prefall phase, the critical phase, the postfall phase and the recovery phase.
During the “prefall” phase the person performs usual activities of daily living (ADL), with occasional sudden movements, like sitting or lying down rapidly, which must be distinguished from a fall.
The “critical phase” consists in the sudden movement of the body toward the ground, ending with a vertical shock on the ground. The duration of this phase is
Industrial developments and commercial products
Some of the previous results were successfully implemented in functional prototypes, and an extensive range of patents has been filed in the fall sensor area. In a few cases, final products were built and are available on the market place.
For the sake of clarity, we first propose a classification of the patents with a specific terminology (Table 1).
Using our terminology, we attempted to classify most of the existing patents (Table 2).
We observed a regular increase in the submission of patents,
Evaluation of the fall sensors
Most of the academic studies started with the design of a test instrument in order to record signals, or images, during simulated situations of fall/non fall. Eventually, they suggested algorithms, which were tested off-line on recorded signals/images. But few studies reported result data and conditions of assessment.
Lord and Colvin [29] reported in 1991 some early studies on fall detection using accelerometers with very little details. Nait-Charif [10] and Rougier and Meunier [11] announced
Conclusion
The ideal fall detection system should exhibit both sensitivity and specificity reaching 100%. This was sometimes reached in experimental set ups [17], [22], but when developed as an autonomous integrated fall sensor, there is a dramatic loss in performances [28], [31]. No individual method seems satisfactory, it thus seems appropriate that a combination of several methods would allow for a greater selectivity. Also, a multidimensional approach, combining both kinematics and physiological
Acknowledgements
The authors wish to thank Dr. Pierre-Emanuel Colle, senior lecturer and head of the Grenoble Medical school language department, for his extensive corrections of the article.
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2022, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :Falls usually consume shorter time from start to end, relative to normal action such as walking and squatting, but it will bring about long-lasting negative effects on the elderly, including physical injury and serious psychological barriers [3–5]. If the elderly can be found and treated earlier after a fall, it is helpful to the later recovery of the injured [6]. However, there are more and more elderly people living alone, which makes it difficult to provide timely assistance to the elderly who have fallen.