2015
DOI: 10.3390/s150817827
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Analysis of Android Device-Based Solutions for Fall Detection

Abstract: Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy weara… Show more

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Cited by 69 publications
(41 citation statements)
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References 148 publications
(196 reference statements)
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“…This method allows moving the threshold just below the minimum fall values (as T 2 does) [12]. Table 7 shows the specificity and accuracy obtained after a 10-fold cross-validation with all 38 subjects (sensitivity achieved approx.…”
Section: Zero False Negativesmentioning
confidence: 99%
“…This method allows moving the threshold just below the minimum fall values (as T 2 does) [12]. Table 7 shows the specificity and accuracy obtained after a 10-fold cross-validation with all 38 subjects (sensitivity achieved approx.…”
Section: Zero False Negativesmentioning
confidence: 99%
“…This positions (specially pocket-or thigh-, waist and chest) include the most typical sensor placements that are commonly considered by the related literature (see [37] or [42] for a further analysis of this matter).…”
Section: Resultsmentioning
confidence: 99%
“…We compared four basic ‘thresholding’ algorithms, which only identify a fall if one or several mobility variables surpass some decision thresholds (simultaneously or in consecutive observation intervals). We just considered these simple and untrained methods as, in a real scenario of real-time-detection, sensor motes may present relevant constrains in terms of computational and storage resources, so that they may pose significant restrictions to the implementation of more sophisticated detection techniques (such as those based on artificial intelligence, rule-based or machine learning techniques) that have been utilized by the research literature (see [11] or [37] for a comprehensive state-of-the-art on smartphone-based FDSs). The four compared algorithms, also considered in [38] and [29], are briefly described in the following paragraphs.…”
Section: Methodsmentioning
confidence: 99%
“…Another major problem raised is the rate of battery consumption when mobile application for continuous monitoring are used. Casilari et al [31] present another survey on the analysis of android based smart phones solutions for fall detection. They systematically classify and compare many algorithms from the literature taking into account different criteria such as the system architecture, sensors used, detection algorithms and the response in case of a false alarms.…”
Section: Survey Of Existing Literature Review On Fall Detectionmentioning
confidence: 99%