Automatic fall detection: Complementary devices for a better fall monitoring coverage

Barralon, Pierre; Dorronsoro, Inigo; Hernández, Erik

doi:10.1109/healthcom.2013.6720745

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Due to the inherent difficulties of testing a FDS with actual body falls (especially falls of older people), only a reduced number of works have examined the effectiveness of a FDS with real-world falls. In [ 4 , 5 , 6 , 7 , 8 ] a small group of older people (from 3 to 20 persons) were tracked during some weeks to check the accuracy of the proposed FDS. In all cases, very few falls (or even no falls) occurred during the monitoring period, so just the capacity of the system to avoid false alarms could be actually assessed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Public Datasets for Wearable Fall Detection Systems

Casilari

Santoyo-Ramón

Cano-García

2017

Sensors

120

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Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

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Section: Introductionmentioning

confidence: 99%

Analysis of Public Datasets for Wearable Fall Detection Systems

Casilari

Santoyo-Ramón

Cano-García

2017

Sensors

120

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An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Casilari

Silva

2022

Measurement

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A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

2019

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The number of older people in western countries is constantly increasing. Most of them prefer to live independently and are susceptible to fall incidents. Falls often lead to serious or even fatal injuries which are the leading cause of death for elderlies. To address this problem, it is essential to develop robust fall detection systems. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. We use acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time-and frequency-domain features and provide them to a classification algorithm. In this paper, we test the performance of four algorithms for classifying human activities. These algorithms are the artificial neural network (ANN), K -nearest neighbors (KNN), quadratic support vector machine (QSVM), and ensemble bagged tree (EBT). New features that improve the performance of the classifier are extracted from the power spectral density of the acceleration. In the first step, only the acceleration data are used for activity recognition. Our results reveal that the KNN, ANN, QSVM, and EBT algorithms could achieve overall accuracy of 81.2%, 87.8%, 93.2%, and 94.1%, respectively. The accuracy of fall detection reaches 97.2% and 99.1% without any false alarms for the QSVM and EBT algorithms, respectively. In a second step, we extract features from the autocorrelation function and the power spectral density of both the acceleration and the angular velocity data, which improves the classification accuracy. By using the proposed features, we could achieve overall accuracy of 85.8%, 91.8%, 96.1%, and 97.7% for the KNN, ANN, QSVM, and EBT algorithms, respectively. The accuracy of fall detection reaches 100% for both the QSVM and EBT algorithms without any false alarm, which is the best achievable performance.

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Automatic fall detection: Complementary devices for a better fall monitoring coverage

Cited by 6 publications

References 17 publications

Analysis of Public Datasets for Wearable Fall Detection Systems

Analysis of Public Datasets for Wearable Fall Detection Systems

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

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