The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment. 


The data files are stored in a comma-separated value (.csv) format.

60 sample files of activities performed by healthy individuals and 30 sample files of activities performed by epileptic patients are present in two separate folders in the .zip file.

The sampling frequency of the headset is 512Hz and each activity is performed for a duration of 20 seconds. Every data file contains raw EEG data in a single column.  

Disclaimer: This data was collected ethically with the consent of relevant local research committees. The anonymity of subjects and confidentiality of their mental health conditions was ensured.


The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.

In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images. 


The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.


This paper describes a sensor fusion technique to localize autonomously unmanned vehicles. In particular, we performed a sensor fusion based on the extended Kalman filter between two commercial sensors. The adopted sensors are ZED2 and Intel T265, respectively; these platforms already perform visual-inertial odometry in their integrated system-on-chip. Since these 2 devices represent the top of the range on the market to make an autonomous localization, this study aims to analyze and inform about results that can be obtained by performing a sensor fusion between the two cameras.


Human Activity Recognition (HAR) is the process of handling information from sensors and/or video capture devices under certain circumstances to correctly determine human activities. Nowadays, several simple and automatic HAR methods based on sensors and Artificial Intelligence platforms can be easily implemented.

In this challenge, participants are required to determine the nurse care daily activities by utilizing the accelerometer data collected from the smartphone, which is the cheapest and easy-to-implement way in real life.

Last Updated On: 
Sat, 05/08/2021 - 17:25
Citation Author(s): 
Sayeda Shamma Alia, Kohei Adachi, Paula Lago, Le Nhat Tan, Haru Kaneko, Sozo Inoue

This Matlab model and the included results are submitted as reference for the paper ''. 

Presenting a comparative study of the Sequential Unscented Kalman Filter (SUKF), Least-squares (LS) Multilateration and standard Unscented Kalman Filter (UKF) for localisation that relies on sequentially received datasets. 

The KEWLS and KKF approach presents a novel solution using Linear Kalman Filters (LKF) to extrapolate individual sensor measurements to a synchronous point in time for use in LS Multilateration. 



Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the corners of the testbed and markers are placed at every 45 cm. RSSI measurements done on the grid shown in Figure are stored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop, which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms.


Data  are  collected  on  a  5m×10msized  test  bed,  which  is  set  up  at  Kadir  Has  University,Istanbul. Wireless access points are located around the cornersof  the  test  bed  and  markers  are  placed  at  every  45  cm.RSSI  measurements  done  on  the  grid  shown  in  Figure  2  arestored via NetSurveyor program running on a Lenovo IdeapadFLEX  4  laptop,  

which  has  an  Intel  Dual  Band  Wireless-AC8260 Wi-Fi adaptor.At  each  measurement  point,  RSSI  data  are  collected  for1  min  with  a  sampling  interval  of  250  ms.  XML file is read with MATLAB for data of full area and applied trajectory.


Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.


The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.


This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion.


The dataset contains Multivariate Time Series (MTS) of the miniature vehicle’s in and out movement in the agricultural field. The miniature vehicle collects the sensory data of the Inertial Measurement Unit (IMU) sensor (MPU-9250) deployed on it. MPU-9250 is a 9-axis sensor used for recording the linear and angular motion of the vehicle in the jerking condition due to the uneven surface of the farmland. MPU-9250 comprises a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. These sensors are connected to a NodeMCU with an attached SD card, which stores the data. The sensory data is collected from sixteen different agricultural fields at a sampling rate of 5 Hz for 5 minutes each. Therefore, each field produces 1500 instances of tri-axial sensors (accelerometer, gyroscope, and magnetometer). Hence, the total instances we have collected is 1500 X 16 =24000.



This gas consumption dataset is provided by a Chinese gas company. It contains three years of gas consumption data with respect to five canteen users.