As an alternative to classical cryptography, Physical Layer Security (PhySec) provides primitives to achieve fundamental security goals like confidentiality, authentication or key derivation. Through its origins in the field of information theory, these primitives are rigorously analysed and their information theoretic security is proven. Nevertheless, the practical realizations of the different approaches do take certain assumptions about the physical world as granted.
The data is provided as zipped NumPy arrays with custom headers. To load an file the NumPy package is required.
The respective loadz primitive allows for a straight forward loading of the datasets.
To load a file “file.npz” the following code is sufficient:
import numpy as np
measurement = np.load(’file.npz ’, allow pickle =False)
header , data = measurement [’header ’], measurement [’data ’]
The dataset comes with a supplementary script example_script.py illustrating the basic usage of the dataset.
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.
Follow instruction in readme file