A smartphone-based fall detection system

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.

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“…98.6% and 99.6% by Yuwono et al [14]; and 100% and 100% by Abbate et al [15]. Other fall detection studies provide similar performances [16,17].…”

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confidence: 76%

A comparison of public datasets for acceleration-based fall detection

Igual

Medrano

Plaza

2015

Medical Engineering & Physics

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“…For this reason the proposed system is also available for hikers and occasional workers in isolated scenarios. [13], [18]. Future development of this work will include also specific solutions for elderliness, for people with degenerative diseases and for people that live in areas with a limited access to emergency services.…”

Section: Resultsmentioning

confidence: 99%

Risk management in solitary agricultural work: new technologies for handling emergency and falls from great heights (SHADE)

Boscariol¹,

Moro²,

Fanzutto³

et al. 2015

ces

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Solitary work and agricultural activities are the scenarios of a large number of severe injuries and deaths, also because first aid may be difficult to achieve in isolated locations. This work proposes a technology available on smartphones that allows triggering an emergency call when a fall from height or an unconsciousness state is detected. The results of several tests, which include different detection algorithms and scenarios, are reported in this work. Tests performed with the aid of a dummy have allowed developing a reliable algorithm for the detection of dangerous situations. This system is available as an Android application.

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“…6 (c), a small angular velocity was generated when the forefeet touched the ground, whereas a large angular velocity was generated when the heels touched the surface. (0.385 rad), S_Jo_S, S_Ju_S, and S_Jd_S action sequences, which were reported to be indistinguishable from a fall [17,19], were correctly recognized as ADL events. for eight falls and ADL action sequences.…”

Section: Imentioning

confidence: 90%

“…4 (c), jogging can be modeled as small jump. When free phases between steps are shorter, landing peaks are often made of two or more overlapped sub-peaks owing to the landing foot starting a new leap [19]. As shown in Figs.…”

Section: Imentioning

confidence: 99%

Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope

Park¹,

Kim²,

Baik³

et al. 2017

Journal of Sensor Science and Technology

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A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (ω res ), and angular variation (θ res ) were calculated using MATLAB. When the measured values of SVM_Acc, ω res , and θ res were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ω res was larger than 1.75 rad/s (TH2), and θ res was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ω res , and θ res were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences. In this study, a threshold-based fall recognition algorithm using Received: Jan. 19, 2017, Revised: Jun. 23, 2017, Accepted: Jan. 30, 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/ licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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A smartphone-based fall detection system

Cited by 311 publications

References 17 publications

A comparison of public datasets for acceleration-based fall detection

A comparison of public datasets for acceleration-based fall detection

Risk management in solitary agricultural work: new technologies for handling emergency and falls from great heights (SHADE)

Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope

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