Table 6.

Table summarizing the datasets previously discussed, classified according to the number of participants, the acquisition system, the detected parameters, and the suggested application.

Dataset	Provided by	No of Participants	Parameters	Approach	MoCap System Details	Suggested Application
UI–PRMD [125]	University of Idaho	10	Locations and angular orientations of the body joints	Vision-based	Vicon optical trackers Kinect cameras	Monitoring rehabilitation exercises
KIMORE [126]	Marche Polytechnic University	78	Joint locations	Vision-based	Kinect cameras	Detection motor dysfunction
M. Capecci et al. [127]	Marche Polytechnic University	7	Joint locations	Vision-based	Kinect v1	Evaluation of karate moves
Daily and sports activities data set [128]	Bilkent University	8	Inertial data	Sensor-based	Inertial sensors (25 Hz sampling frequency)	Activity recognition
Human Activity recognition using smartphones data set [129]	University of Genoa	30	Inertial data	Sensor-based	Smartphone (Samsung Galaxy S II)	Activity recognition
MoVi dataset [130]	York University	90	Camera images, joint locations, inertial data	Vision-based Sensor-based	15 cameras (Qualisys Oqus 300 and 310) 2 stationary cameras (RGB Grasshopper2) 2 hand-held cameras (iPhone 7) 17 IMU sensors (Noitom Neuron Edition V2)	Motion recognition
Gait in aging and disease database [131]	PhysioBank	15	Stride interval	Sensor-based	Force-sensitive resistors	Normal gait and Parkinson’s disease analysis
MIT database [132]	MIT	24	View, time	Vision-based	Sony Handycam	Gait recognition
Georgia Tech [133]	Georgia Tech	20	View, time, distance	Vision-based	-	Gait recognition