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 Francisco Javier Díaz-Pernas Henning Müller Cristina Simón-Martínez
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 activity in clinical and research settings.
Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab
settings, it is necessary to use a reduced number of devices for movement
acquisition. Promising solutions in this context are IMU-based wearables and
single camera systems. Additionally, the development of machine learning
systems able to recognize and digest clinically relevant data in-the-wild is
needed, and therefore determining the ideal input to those is crucial.