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Sensors 2017, 17(7), 1513;

Analysis of Public Datasets for Wearable Fall Detection Systems

Departamento de Tecnología Electrónica, Universidad de Málaga, ETSI Telecomunicación, 29071 Málaga, Spain
Author to whom correspondence should be addressed.
Academic Editor: Edward Sazonov
Received: 18 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 27 June 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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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. View Full-Text
Keywords: fall detection; dataset; accelerometer; wearable; smartphone; mHealth fall detection; dataset; accelerometer; wearable; smartphone; mHealth

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Casilari, E.; Santoyo-Ramón, J.-A.; Cano-García, J.-M. Analysis of Public Datasets for Wearable Fall Detection Systems. Sensors 2017, 17, 1513.

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