Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

Department of Electronics, Computer Science and Systems, University of Bologna, Bologna, Italy.
PLoS ONE (Impact Factor: 3.23). 05/2012; 7(5):e37062. DOI: 10.1371/journal.pone.0037062
Source: PubMed


Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean ± std) 83.0% ± 30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0% ± 27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.

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    • "These systems (e.g. fall detectors [7] [8] [9]) have unmatched preventive impact to reduction both human anguish and intervention costs at the same time allowing for sovereign living. Yet another concept applies the human tracking systems for behavioral studies [10] [11] [12]. "
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    ABSTRACT: Smart sensors provide a support for turning a typical household Into a technology fitted environment for elderly or disabled who need tracking or assistance with activities of daily life. This paper presents a design of inhouse infrastructure of sensorized appliances making a measurement context for long-term studies of behavior and early detection of health setback. Each appliance-specific sensor set reports a multimodal data on the position and usage of the device as well as operating people. Besides seamless security watching, these data are integrated in personalized behavioral records (actigrams) specifying activities of daily living. This record is then automatically explored to identify patterns of circadian sequences to assess a lifestyle and provide a reference for detection of abnormalities. The purpose of newly proposed monitoring system is twofold: increasing the security of independently living elderly and identifying their habits and interpreting inhabitual actions as a possible health setback. The system uses remotely configurable sensors detecting human activity by collecting simple information in a pervasive, imperceptible way.
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    • "It should be noted that the detection rates provided by all these studies are very high. However, many authors on this field have noticed strong difficulties when comparing different acceleration-based studies [6] [18]. This is due to the fact that each study uses its own dataset composed of simulated falls and ADL. "
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    ABSTRACT: Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
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    • "From the perspective of threshold, the simplest fall detection method is based on an acceleration threshold alone (Perry et al., 2009). However, a threshold-only approach would misclassify activities and result in false positives (Bagalà et al., 2012), as some normal daily activities also produce large acceleration impact. To improve the fall detection performance, other parameters are combined by fall detection systems while a single accelerometer is worn. "
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    ABSTRACT: Threshold-based fall detection has been widely adopted in conventional fall detection systems. In this paper, we argue that a fixed threshold is not flexible enough for different people. By exploiting the personalised and adaptive threshold, we propose a novel threshold extraction model, which meets being adaptive to detect a fall, while only taking consideration of data from activity of daily living (ADL). We believe this is a solid step toward improving the performance of the threshold-based fall detection solution. Furthermore, we incorporate the proposed idea into Chameleon. To evaluate the performance of this threshold extraction model, we compared Chameleon with advanced magnitude detection (AMD) and fixed and tracking fall detection (FTFD). The results show Chameleon has an accuracy of 96.83% when detecting falls, which is 1.67% higher than FTFD and 2.67% higher than AMD. Meanwhile, the sensitivity and the specificity of Chameleon are also higher than the other two algorithms. Reference to this paper should be made as follows: Ren, L. and Shi, W. (xxxx) 'Chameleon: personalised and adaptive fall detection of elderly people in home-based environments', Int.
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