Challenges, issues and trends in fall detection systems

By Raúl Igual, Carlos Medrano, Inmaculada Plaza. BioMedical Engineering OnLine, 2013

633 citations recorded by the source corpus.

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c733a0ade04154f164b6d3714c20a79167e523c7

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Citations in the corpus

SisFall: A Fall and Movement Dataset (2017, background): “Smartphones are a popular selection for authors because they include a robust hardware, a powerful processor, and they are economically affordable [6,11].”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “For instance, the survey by Igual et al. (2013) noted that low-cost cameras and accelerometers embedded in smartphones may oﬀer the most sensible technological choice for the investigation of fall detection.”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, background): “Such a value is assumed very often as a decision threshold in simple threshold-based algorithms for fall detection [10, 8].”
A Depth-Based Fall Detection System Using a Kinect® Sensor (2014, background): “intervention allows decreasing the probability of complications derived from physical damage due to the accident (such as those caused by fractures of the lower or upper limbs) [1].”