UWB Motion Detection Data Set

Introduction

This data set includes a collection of measurements using DecaWave DW1000 UWB radios in two indoor environments used for motion detection functionality. Measurements include channel impulse response (CIR) samples in form of power delay profile (PDP) with corresponding timestamps for three channels for each indoor environment.

Data set includes pieces of Python code and Jupyter notebooks for data loading, analysis and to reproduce the results of a paper entitled "UWB Radio Based Motion Detection System for Assisted Living" submitted to MDPI Sensors.

The data set will require around 10 GB of total free space after extraction.

The code included in the data set is written and tested on Linux (Ubuntu 20.04) and requires 16 GB of RAM and additional SWAP partition to run properly. The code can be modified to consume less memory but it requires unnecessary additional work. If the .npy format is compatible with your numpy version, you won't need to regenerate npy data from .csv files.

Data Set Structure

The resulting folder after extracting the uwb_motion_detection.zip file is organized as follows:

• data subfolder: contains all original .csv and intermediate .npy data files.
  • models
    • pdp: this folder contains 4 .csv files with raw PDP measurements (timestamp + PDP). The data format will be discussed in the following section.
    • pdp_diff: this folder contains .npy files with PDP samples and .npy files with timestamps. Those files are generated by running the generate_pdp_diff.py script.
    • generate_pdp_diff.py
  • validation subfolder: contains data for motion detection validation
    • events: contains .npy files with motion events for validation. The .npy files are generated using generate_event_x.py files or notebooks inside the /Process/validation folder.
    • pdp: this folder contains raw PDP measurements in .csv format.
    • pdp_diff: this folder contains .npy files with PDP samples and .npy files with timestamps. Those files are generated by running the generate_pdp_diff.py script.
    • generate_events_0.py
    • generate_events_1.py
    • generate_events_2.py
    • generate_pdp_diff.py
• figures subfolder: contains all figures generated in Jupyter notebooks inside the "Process" folder.
• Process subfolder: contains Jupyter notebooks with data processing and motion detection code.
  • MotionDetection: contains notebook comparing standard score motion detection with windowed standard score motion detection
  • OnlineModels: presents the development process of online models definitions
  • PDP_diff: presents the basic principle of PDP differences used in the motion detection
  • Validation: presents a motion detection validation process

Raw data structure

All .csv files in data folder contain raw PDP measurements with timestamps for each PDP sample. The structure of file goes as follows:

unix timestamp, cir0 [dBm], cir1 [dBm], cir2[dBm] ... cir149[dBm]