11 citations · 21 across the 4 of their papers we have counts for
4 papers · 1 filter
Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities
Mario Medrano-Paredes, Carmen Fernández-González, Francisco-Javier Díaz-Pernas +3
Advances in machine learning and wearable sensors offer new opportunities for capturing and analyzing human movement outside specialized laboratories. Accurate assessment of human…
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
Mario Martínez-Zarzuela, David González-Ortega, Míriam Antón-Rodríguez +3
The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical acti…
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
Mario Martínez-Zarzuela, Javier González-Alonso, Míriam Antón-Rodríguez +3
Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movem…
Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras
M. Martínez-Zarzuela, M. Pedraza-Hueso, F. J. Díaz-Pernas +2
This article describes the design and development of a system for remote indoor 3D monitoring using an undetermined number of Microsoft(R) Kinect sensors. In the proposed client-se…