A survey on fall detection: Principles and approaches

By Muhammad Mubashir, Ling Shao, Nicholas Luke Seed. 2013

896 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, neutral): “However, visual data affect the privacy of inhabitants, which calls for privacy preserving approaches in videos and images [54, 63-65].”
A Survey on Ambient Intelligence in Health Care (2013, neutral): “Fall detection techniques rely on several technologies: wearable devices, ambient sensors, and cameras [147].”
UP-Fall Detection Dataset: A Multimodal Approach (2019, background): “[6], divided the approaches for fall detection in three categories: wearable device-based, ambience sensor-based, and vision-based.”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, background): “Therefore, significant attention has been devoted to developing an efficient wearable system for human fall detection [6, 7, 8, 9].”
Fall detection using ceiling-mounted 3D depth camera (2015, methodology): “, 2005) and therefore significant effort has been devoted to ensuring user-friendly assistive devices (Mubashir et al., 2013).”
A Survey on Ambient-Assisted Living Tools for Older Adults (2013, background): “categories: wearable device based, ambience sensor based, and camera (vision) based [152].”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, background): “…fall detection techniques usually accomplish their task by analizing samples from sensors, which can be physically deployed in the ambient (ambient sensors, e.g., cameras, vibration sensors, and microphones) or worn by people (wearable sensors, e.g., accelerometers, gyroscopes, and barometers) [6].”
Computer Vision and Image Understanding Computer Vision for Assistive Technologies (neutral): “An example of problem where a combination of the different signals can help to improve reliability of the assistive system is fall detection ( Mubashir et al., 2013 ).”
Challenges, issues and trends in fall detection systems (2013, methodology): “In our study, we do not present a detailed discussion of each paper, like [12], but rather we show the information schematically by means of tables including relevant information for people conducting research in this field: the types of falls used in the simulations, the number of users involved in”
WiFall: Device-free fall detection by wireless networks (2017, background): “Noury et al. [1], Yu [12] and Mubashir et al. [13] review the principles and approaches used in existing fall detection (FD) systems .”
Detecting Falls with Wearable Sensors Using Machine Learning Techniques (2014, neutral): “Fall detection is surveyed in [9,10].”