Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

Table 2

Human fall-based open datasets.

Dataset/year	Sensors	Number of subjects (age)	Total samples	Position of sensing points	Scenario
UP-Fall (2019)	A, C, E, L, IR, G	17 (18–24)	561	H, F, N, Wa, Wr, An	Lab
SisFall (2017)	A, G	38 (19–75)	4505	Wa	Gym, hall
UniMiB SHAR (2017)	A	30 (18–60)	7013	T	N/A
NTU (2016)	K	40 (10–35)	56000	Ce	Lab
UMA Fall (2016)	A, G, M	17 (18–35)	531	An, Ch, T, Wa, Wr	Home
MobiAct (2016)	A, G,O	57 (22–47)	2526	T	Gym, hall
MobiFall (2013)	A, G, O	24 (22–47)	630	T	Gym, hall

Note. N/A: not appropriately defined; C: RGB camera; A: accelerometer; G: gyroscope; O: orientation measurements; K: Kinect sensor; M: magnetometer; IR: infrared sensor; L: luminosity sensor; E: electroencephalography (EEG) headset; Ce: ceiling; T: thigh (pocket); Wa: waist; Wr: wrist; An: ankle; Ch: chest; H: head; N: neck; F: floor.