Personalization and Adaptation to the Medium and Context in a Fall Detection System

Naranjo-Hernández, David; Roa, Laura M.; Reina‐Tosina, Javier; Valderrama, Miguel

doi:10.1109/titb.2012.2185851

Cited by 26 publications

(10 citation statements)

References 32 publications

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“…The second-generation algorithm reached a sensitivity of 80% and a specificity of 99.9985%. These increased metrics are within the sensitivity ranges (62%–100%) and specificity range (87% and 100%) reported for similar studies [18,49,50,51]. The sensitivity of the second-generation further increased to 100%, if falls with consequences are considered.…”

Section: Discussionsupporting

confidence: 80%

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Scheurer

Koch

Kučera

et al. 2019

Sensors

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Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.

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Section: Discussionsupporting

confidence: 80%

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Scheurer

Koch

Kučera

et al. 2019

Sensors

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“…For a preliminary test of the developed application, a smart fall detector [20] has been used. For the design of the graphic interface of the application, several questionnaires and interviews have been made to the final users with the objective of detecting possible needs or requirements.…”

Section: Methodsmentioning

confidence: 99%

Design and implementation of a standardised framework for the management of a wireless body network in an Mobile Health environment

Talaminos

Naranjo

Barbarov

et al. 2017

Healthcare Technology Letters

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The design and implementation of a framework that facilitates the development of Mobile Health applications to manage the communications with biomedical sensors in compliance with the CEN ISO/IEEE 11073 standard family are presented. The framework includes a set of functional modules that are responsible, among other tasks, of the communication of sensors and the processing and storage of data. The mobile terminal acts as an intermediary or hub, collecting and presenting the data received in a standardised way, regardless of the sensor type used. In this context, as proof of concept it is presented a mobile app built on the top of the framework to manage the communications with a smart fall detector.

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“…In the vast majority of fall related studies evaluation process is mainly performed by healthy volunteers or based on simulation [38,60,61]. This fact makes it almost impossible to give an accurate assessment for operational capabilities of developed system or reliability of deployed algorithm.…”

Section: Wearable Sensors Fusionmentioning

confidence: 99%

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

2016

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Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

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Personalization and Adaptation to the Medium and Context in a Fall Detection System

Cited by 26 publications

References 32 publications

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Design and implementation of a standardised framework for the management of a wireless body network in an Mobile Health environment

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

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