Fall detection - Principles and Methods

By Norbert Noury, Anthony Fleury, P. Rumeau, A. Bourke, G. Ó. Laighin, V. Rialle, J. Lundy. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

637 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a992ba145aba966ed0ffbde827d6be637f969020

How researchers see this paper

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

Detecting Falls with Wearable Sensors Using Machine Learning Techniques (2014, background): “We found only three works that provide guidelines for fall experiments [21–23] and only one that pursues them [8].”
A Microphone Array System for Automatic Fall Detection (2012, background): “Wearable devices, like accelerometer-based ones [5]–[8], detect falls by measuring the applied acceleration along the vertical axis.”
A survey on fall detection: Principles and approaches (2013, methodology): “[56] and Yu [55] reviewed principles and methods used in existing fall detection approaches.”
SisFall: A Fall and Movement Dataset (2017, methodology): “The sensitivity (SE), speciﬁcity (SP) and accuracy (AC) were computed as follows [26]: where TP and TN are the true positives and negatives; FP and FN the false positives and negatives, respectively.”
Data Fusion in Health Smart Home: Preliminary Individual Evaluation of Two Families of Sensors (2008, methodology): “The former, s uch as embedded accelerometers, magnetometers and gyroscopes are used to deliver information on the posture and movements of the person (walking, sitting down…) [2], [3] or to try to automatically detect a fall [4], [ 5 ].”
3D Tracking via Body Radio Reflections (2014, background): “Current solutions to this problem include inertial sensors which old people tend to forget to wear [15], or cameras which infringe on privacy, particularly in bed-rooms and bathrooms [20].”
RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, methodology): “Index Terms—Fall detection, activity recognition, channel state information (CSI), WiFi, phase difference Ç”
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation (2011, methodology): “Currently, two main technologies are used to detect falls: wearable devices [2]–[4], [7] and camera based devices [8]– [18].”
A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls (2009, background): “Moreover, these devices produce many false alarms [ 10 ], and old people tend to forget wearing them frequently.”
Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016, background): “Fall detection of older adults is a major health risk and several systems have been proposed for the automatic early detection and prevention of such emergency cases [49–51].”
Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, methodology): “…implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN).”
Graph Distillation for Action Detection with Privileged Modalities (2017, background): “For example, surveillance systems for fall detection [40,64] often face the challenge that annotated videos of fall incidents are hard to obtain, and more importantly, yhr”
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].”
Challenges, issues and trends in fall detection systems (2013, background): “This is sometimes reached in experimental environments, but when applied to a real situation, the detection rate decreases [8].”