Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

By Qiang Li, J. Stankovic, M. Hanson, Adam T. Barth, John C. Lach, Gang Zhou. 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009

592 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5f63e158d41e36398da75325ff9e1ae1b1200977

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, background): “To train and evaluate their techniques, researchers usually build their own dataset of samples and rarely make it publicly available [7, 8, 9].”
Mercury: a wearable sensor network platform for high-fidelity motion analysis (2009, methodology): “These include feature extraction [17, 19, 37], gait analysis [40, 44], fall detection [7, 12, 25], and activity classifiers [5, 15, 18, 33].”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “Later, Li et al. (2009) investigated fusion of gyroscope and accelerometer data for the classiﬁcation of falls and non-falls.”
A viewpoint-independent statistical method for fall detection (2012, methodology): “Several existing methods use non-vision sensors, such as the accelerometer [6, 7, 9], oftentimes combined with other devices such as gyroscopes [9] and microphone [7].”
Fall detection system using Kinect’s infrared sensor (2014, background): “Normally, such systems use wearable motion detectors with accelerometers and gyroscopes [7, 20] capable of detecting the rapid motion changes of the person who wears them.”
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors (2012, background): “In [16], the rotation angle produced by gyroscope is identified to be the key performance booster for fall detection.”
A survey of depth and inertial sensor fusion for human action recognition (2015, methodology): “In [41], a fall detection system was presented based on wearable inertial sensors.”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Accelerometer-based wearable devices have been developed cce p r (Li et al. 2009), and monitoring activities of daily living (Spenkelink et al. 2002).”
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time (2017, background): “…such as straps, belts, wristbands, or other accessories to prevent relative motion between the sensors and the parts of the human body [59–63 , travel distance velocity, and energy expenditure can be estimated by a foot-worn accelerometer [58,68,69,74]Sensors can also be located around…”
Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing (2015, methodology): “This has been used in the majorities of existing studies [3, 8, 11, 17].”