The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones

Vavoulas, George; Pediaditis, Matthew; Spanakis, Emmanouil G.; Tsiknakis, Manolis

doi:10.1109/bibe.2013.6701629

Cited by 86 publications

(45 citation statements)

References 11 publications

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“…Moreover a modified algorithm is also described in order to further increase the battery life-time. Furthermore, experiments conducted on the public dataset MobiFall [17] confirm the good performance of the proposed fall detection algorithm and its superiority over other existing algorithms based on smartphone sensors, for fall detection.…”

Section: Introductionmentioning

confidence: 54%

“…The MobiFall dataset [17] is a publicly available dataset, that was built with the objective of testing new methods and doing comparative evaluation among different fall detection algorithms, based on smartphone sensors. Therefore for further evaluating the performance of the methods described herein and for comparison with the performance of other existing fall detection algorithms (precisely those that are chosen in [17] for a comparative evaluation using the MobiFall dataset, and that are based on mobile phones or smartphone devices only), we carried out experiments in the MobiFall dataset [17].…”

Section: Comparative Studymentioning

confidence: 99%

“…Therefore for further evaluating the performance of the methods described herein and for comparison with the performance of other existing fall detection algorithms (precisely those that are chosen in [17] for a comparative evaluation using the MobiFall dataset, and that are based on mobile phones or smartphone devices only), we carried out experiments in the MobiFall dataset [17]. A short description of this dataset is given in the next subsection.…”

Section: Comparative Studymentioning

confidence: 99%

“…A short description of this dataset is given in the next subsection. [17] for a detailed description. The MobiFall dataset contains data collected by six male and five female subjects, whose ages ranged from 22 to 36.…”

Section: Comparative Studymentioning

confidence: 99%

See 3 more Smart Citations

Exploring smartphone sensors for fall detection

et al. 2016

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Falling, and the fear of falling, is a serious health problem among the elderly. It often results in physical and mental injuries that have the potential to severely reduce their mobility, independence and overall quality of life. Nevertheless, the consequences of a fall can be largely diminished by providing fast assistance. These facts have lead to the development of several automatic fall detection systems. Recently, many researches have focused particularly on smartphone-based applications. In this paper, we study the capacity of smartphone built-in sensors to differentiate fall events from activities of daily living. We explore, in particular, the information provided by the accelerometer, magnetometer and gyroscope sensors. A collection of features is analyzed and the efficiency of different sensor output combinations is tested using experimental data. Based on these results, a new, simple, and reliable algorithm for fall detection is proposed. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates 100 % for sensitivity and 93 % for specificity. Furthermore, evaluation conducted on a public dataset, for comparison with other existing smartphone-based fall detection algorithms, shows the high potential of the proposed method.

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Section: Introductionmentioning

confidence: 54%

Section: Comparative Studymentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

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Exploring smartphone sensors for fall detection

et al. 2016

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“…Some efforts have been performed in this direction since several datasets were made publicly available in the recent years: DLR [21] published in 2011, MobiFall [22] available in 2013 and tFall [20] uploaded in 2014 (the study of Fudickar et al [23] cites another public dataset but it seems that it cannot be downloaded currently). Although these three datasets can be freely accessed, there is no study focused on comparing them.…”

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confidence: 99%

A comparison of public datasets for acceleration-based fall detection

Igual

Medrano

Plaza

2015

Medical Engineering & Physics

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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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Using Deep Learning and Smartphone for Automatic Detection of Fall and Daily Activities

Cheng

Chu

et al. 2019

Smart Health

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The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones

Cited by 86 publications

References 11 publications

Exploring smartphone sensors for fall detection

Exploring smartphone sensors for fall detection

A comparison of public datasets for acceleration-based fall detection

Using Deep Learning and Smartphone for Automatic Detection of Fall and Daily Activities

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