We upload the orig.inal measured ecg and impedance data, in xlxs forms, the 1st column is sampling time,the second is amplitude. We use these files and OriginPro to  verify the correctness of the model. And use matlab to verify the algorithm's  effectiveness.. More information if you needed,, pls connect the following email: 17112020021@fudan.edu.cn




This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons.


The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.


Data Description

This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. 

Filename: Exoskeleton_TweetIDs_Set1.txt

Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022

Filename: Exoskeleton_TweetIDs_Set2.txt

Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021

Filename: Exoskeleton_TweetIDs_Set3.txt

Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020

Filename: Exoskeleton_TweetIDs_Set4.txt

Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020

Filename: Exoskeleton_TweetIDs_Set5.txt

Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019

Filename: Exoskeleton_TweetIDs_Set6.txt

Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019

Filename: Exoskeleton_TweetIDs_Set7.txt

Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017


For any questions related to the dataset, please contact Nirmalya Thakur at thakurna@mail.uc.edu



The dataset contains temperature measurements taken with an 8x8 infrared array (Panasonic Grid-EYE) over a period of three weeks during 2018 in Bucharest, Romania, in an educational facility.


The Firearm Recoil Dataset was collected utilizing a wrist worn accelerometer to record the recoil generated from one subject’s use of 15 different firearms of the Handgun, Rifle and Shotgun class. The type of the firearm based on its ability to auto-load or not is also denoted. 


Datasets are broken up into seperate CSV files for each individual firearm. Details associated with the firearm utilized and data collection specifications is outlined in the Readme File. If you use this datasets for your research, please cite the following paper:

Md. Abdullah Al Hafiz Khan, David Welsh, and Nirmalya Roy. Firearm Detection Using Wrist Worn Tri-Axis Accelerometer Signals, in Proceedings of the 4th Workshop on sensing systems and applications using wrist worn smart devices (WristSense’18), co-located with PerCom, March 2018


Skeleton datasets for Normal, Antalgic, Stiff legged, Lurching, Steppage, and Trendelenburg gaits.


This dataset includes highway driving scenes in Daejeon, South Korea.


This dataset contains actual field/experimental data for the following environmental engineering applications, namely:

  • Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
  • Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.

The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This dataset is the result of one of the first comprehensive studies of static Bluetooth AoA-based Indoor Localization in a real-world testbed using commercial off-the-shelf Bluetooth chipsets.

The positioning experiments were carried out on a 100 m² test area using four stationary Bluetooth sensor devices each equipped with eight antennas. With this setup, a median localization accuracy of up to 18 cm was achieved.


We design a solution to achieve coordinated localization between two unmanned aerial vehicles (UAVs) using radio and camera perception. We achieve the localization between the UAVs in the context of solving the problem of UAV Global Positioning System (GPS) failure or its unavailability. Our approach allows one UAV with a functional GPS unit to coordinate the localization of another UAV with a compromised or missing GPS system. Our solution for localization uses a sensor fusion and coordinated wireless communication approach.


There are two data files, named 'Data1.mdb' and 'Data2.mdb'. A total of 87,272 pieces of data, including 43,607 pieces of data in file 'Data1.mdb' and 43,665 pieces of data in file 'Data2.mdb'. Please open them with ACCESS software.