Human fall detection on embedded platform using depth maps and wireless accelerometer

618 citations recorded by the source corpus.

How researchers see this paper

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

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “2 UR fall detection dataset University of Rzeszow created UR Fall Detection dataset in 2014 [51], which devotes to detecting and recognizing human falls .”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, background): “This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [32], Ugulino [33], USC HAD [34], DaLiAc [10], EvAAL [35], MHEALTH [36], UCI ARSA [27], BaSA [37], UR Fall Detection [38], MMsys [9], SisFall [39], UMA Fall 4 [18], and REALDISP [40].”
Multimodal biomedical AI (2022, background): “…ambient sensors (such as depth cameras and microphones) with wearables data (for example, accelerometers, which measure physical activity) has the potential to improve the reliability of fall detection systems while keeping a low false alarm rate 53 , and to improve gait analysis performance 54 .”
RGBD Datasets: Past, Present and Future (2016, background): “Three datasets of humans falling over [60, 39, 38] reflect an interest in use of RGBD sensors for monitoring vulnerable humans in their daily lives.”
Elderly Fall Detection Systems: A Literature Survey (2020, methodology): “In the work by Kwolek and Kepski (2014, 2016), wearable devices and Kinect were connected to the Pandaboard through Bluetooth and cable, separately.”