Fall detection system using Kinect’s infrared sensor

By G. Mastorakis, Dimitrios Makris. Journal of Real-Time Image Processing, 2014

383 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ca8fc144c982a490f8c6057b7d9efcd3c0fdbbf0

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Citations in the corpus

Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset (2016, background): “As proposed in literature, the sensor can be placed in different setups, from the frontal view [5] to the top view [3], often exploiting ad-hoc algorithms to recognize people and detect a fall.”
Challenges, issues and trends in fall detection systems (2013, methodology): “[18] 2012 Real-time fall detection system based on the Kinect sensor The width, height and depth of the human posture, which define a 3D bounding box Backward, forward and sideways falls 8 subjects All falls were accurately detected Infrared sensor No The system requires no pre-knowledge of the scen”
UP-Fall Detection Dataset: A Multimodal Approach (2019, methodology): “[19] one Kinect one height of 204 cm 8 backward, forward, sideways sitting, laying, picking up, sweeping, dusting 6 trials fall; 4 activities slow activities imitate elderly person human 3D bounding box Dovgan et al.”
Fall detection using ceiling-mounted 3D depth camera (2015, methodology): “Recently, Kinect sensor was demonstrated to be very useful in fall detection (Kepski and Kwolek, 2012; Mastorakis and Makris, 2012).”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, methodology): “Recently, Kinect sensor has been proposed to achieve fall detection [20, 21, 22].”
Remote patient monitoring: a comprehensive study (2017, background): “Additionally, in Mastorakis and Makris (2014), IR capabilities available in Kinect sensor have been used.”
MobileRGBD, an open benchmark corpus for mobile RGB-D related algorithms (2014, neutral): “…companion robot, are using RGB-D sensors like the Kinect for many tasks: 3D SLAM, people tracking and skeleton tracking, gesture recognition, body pose estimation, engagement toward a robot or for elderly at home, detecting falls or searching for a fallen person ([2], [3], [4], [5], [6], [7], [8]).”
Evolutionary joint selection to improve human action recognition with RGB-D devices (2014, methodology): “In particular, RGB-D devices are used in ambient assisted living for fall detection (Mastorakis & Makris, 2012), physical rehabilitation (Chang et al., 2011; Huang, 2011), medical image exploration in operating rooms (Gallo et al., 2011) and gait analysis (Stone & Skubic, 2011) among other…”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “Work on demonstrating viability of vision-based approaches have been demonstrated which makes use of infrared cameras (Mastorakis and Makris, 2014), RGB cameras (Charﬁ et al., 2012), and RGB-D depth cameras (Cai et al., 2017).”