SisFall: A Fall and Movement Dataset

Sucerquia, Angela; López, José David; Vargas-Bonilla, Jesús Francisco

doi:10.3390/s17010198

Cited by 304 publications

(129 citation statements)

References 25 publications

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“…In any case, the obtained results (with both specificity and sensitivity near 99%) were better than those published by other studies on FDS that employed the same SisFall dataset as a benchmarking tool [36,52,56,68,69] and in which a specificity and a sensitivity superior to 0.98 were not attained simultaneously.…”

Section: Numerical Resultscontrasting

confidence: 56%

“…Then, we analyzed the resulting network configuration when it was trained and tested with other 13 different datasets (ticked with a check mark in Table 1). We opted to use SisFall [36] as the basis of our analysis as it is one of the most employed datasets in the literature (see Table 4). In addition, it was generated by one of the largest sets of participants (38 subjects including 19 males and 19 females) with the widest age range (19-75 years).…”

Section: Revision and Selection Of Public Datasetsmentioning

confidence: 99%

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A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

Casilari

Lora-Rivera

Garcìa-Lagos

2020

Sensors

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Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.

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Section: Numerical Resultscontrasting

confidence: 56%

Section: Revision and Selection Of Public Datasetsmentioning

confidence: 99%

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

Casilari

Lora-Rivera

Garcìa-Lagos

2020

Sensors

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“…As discussed in Section 1, we considered the datasets acquired from 2012 to be compliant with the year of the older smartphone-based dataset. This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [37], Ugulino [38], USC HAD [39], DaLiAc [10], EvAAL [40], MHEALTH [41], UCI ARSA [32], BaSA [42], UR Fall Detection [43], MMsys [9], SisFall [44], UMA Fall (UMA Fall contains samples from both smartphones and ad-hoc wearable devices.) [23], and REALDISP [45].…”

Section: Adls and Fallsmentioning

confidence: 99%

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

2017

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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.

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“…In this paper, we employ CNNs (also considered in the FDS by He et al in [15]), to evaluate the advantages of introducing the data collected by the gyroscope in the input features of the classifier intended for a wearable FDS. As the benchmarking tool, we employ the SisFall repository [16] (available in [17]), one of the largest public datasets containing accelerometry and gyroscope signals captured during the execution of falls and ADLs. This paper is organized as it follows: after the introduction Section 2 revises the state of the art.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

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Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

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SisFall: A Fall and Movement Dataset

Cited by 304 publications

References 25 publications

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

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