Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings.

Good knowledge about a radio environment, especially about the radio channel, is a prerequisite to design and operate ultra-reliable communications systems. Radio Environment Maps (REMs) are therefore a helpful tool to gain channel awareness. Based on a user’s location, the channel conditions can be estimated in the surrounding of the user by extracting the information from the radio map. This data set contains two measured high-resolution REMs of an indoor environment.

Data augmentation is commonly used to increase the size and diversity of the datasets in machine learning. It is of particular importance to evaluate the robustness of the existing machine learning methods. With progress in geometrical and 3D machine learning, many methods exist to augment a 3D object, from the generation of random orientations to exploring different perspectives of an object. In high-precision applications, the machine learning model must be robust with respect to the small perturbations of the input object.

Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.