The dataset is collected for the purpose of investigating how brainwave signals can be used to industrial insider threat detection. The dataset was connected using Emotiv Insight 5 channels device. The dataset contains data from 17 subjects who accepted to participate in this data collection.
The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.
The data is the circuit design of a sEMG detection system.
We provide a public available database for arcing event detection. We design a platform for arcing fault simulation. The arc simulation is carried out in our local lab under room temperature. A general procedure to collect the arcing and normal current and voltage wave, is designed, which consists of turning on the load, generating arc, stoping arc, turning off the load. The data is collected by a 16bit, 10KHz high resolution recorder and a 12bit, 64000Hz low resolution sensor.
One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental data is time and resource consuming in practice, necessitating new paradigms and efficient models for these data-limited frameworks. Therefore, a novel approach is presented in this work, where a multi-ion-sensing emulator is designed to explain the response of an ion-sensing array in a mixed-ion environment.
The PD-BioStampRC21 dataset provides data from a wearable sensor accelerometry studyconducted for studying activity, gait, tremor, and other motor symptoms in individuals with Parkinson's disease (PD).In addition to individuals with PD, the dataset also includes data for controls that also went through the same study protocol as the PD participants. Data were acquired using lightweight MC 10 BioStamp RC sensors (MC 10 Inc, Lexington, MA), five of which were attached to each participant for gathering data over a roughly two day interval.
Users of the dataset should cite the following paper:
Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong, Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma, "A real-world study of wearable sensors in Parkinson’s disease". Submitted.
where an overview of the study protocol is also provided. Additional detail specific to the dataset and file naming conventions is provided here.
The dataset is comprised of two main components: (I) Sensor and UPDRS-assessment-task annotation data for each participant and (II) demographic and clinical assessment data for all participants. Each of these is described in turn below:
I) Sensor and UPDRS-assessment-task annotation data:
For each participant the sensor accelerometry and UPDRS-assessment-task annotation data are provided as a zip file, for instance, ParticipantID018DataPDBioStampRC.zip for participant ID 018. Unzipping the file generates a folder with a name matching the participant ID, for example, 018, that contains the data organized as the following files. Times and timestamps are consistently reported in units of milliseconds starting from the instant of the earliest sensor recording (for the first sensor applied to the participant).
a) Accelerometer sensor data files (CSV) corresponding to the five different sensor placement locations, which are abbreviated as
1) Trunk (chest) - abbreviated as "ch"
2) Left anterior thigh - abbreviated as "ll"
3) Right anterior thigh - abbreviated as "rl"
4) Left anterior forearm - abbreviated as "lh"
5) Right anterior forearm - abbreviated as "rh"
Example file name for accelerometer sensor data files:
E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv, lh_ID018Accel.csv, and rh_ID018Accel.csv
File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for each measurement and corresponding triaxial accelerometry relative to the sensor coordinate system.
Column 1: "Timestamp (ms)" - Time in milliseconds
Column 2: "Accel X (g)" - Acceleration in X-direction (in units of g = 9.8 m/s^2)
Column 3: "Accel Y (g)" - Acceleration in Y-direction (in units of g = 9.8 m/s^2)
Column 4: "Accel Z (g)" - Acceleration in Z-direction (in units of g = 9.8 m/s^2)
b) Annotation file (CSV). This file provides tagging annotations for the sensor data that identify, via start and end timestamps, the durations of various clinical assessments performed in the study.
Example file name for annotation file:
File format for the annotation file: Comprises of four columns
Column 1: "Event Type" - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of two queries - medication status and MDS-UPDRS assessment body locations
Column 2: "Start Timestamp (ms)" - Start timestamp for the MDS-UPDRS assessments
Column 3: "Stop Timestamp (ms)" - Stop timestamp for the MDS-UPDRS assessments
Column 4: "Value" - Responses to the queries in Column 1 - medication status (OFF/ON) and MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)
II) Demographic and clinical assessment data
For all participants, the demographic and clinical assessment data are provided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzipping the file generates a CSV file named Clinic_DataPDBioStampRCStudy.csv.
File format for the demographic and clinical assessment data file: Comprises of 19 columns
Column 1: "ID" - Participant ID
Column 2: "Sex" - Participant sex (Male/Female)
Column 3: "Status" - Participant disease status (PD/Control)
Column 4: "Age" - Participant age
Column 5: "updrs_3_17a" - Rest tremor amplitude (RUE - Right Upper Extremity)
Column 6: "updrs_3_17b" - Rest tremor amplitude (LUE - Left Upper Extremity)
Column 7: "updrs_3_17c" - Rest tremor amplitude (RLE - Right Lower Extremity)
Column 8: "updrs_3_17d" - Rest tremor amplitude (LLE - Right Lower Extremity)
Column 9: "updrs_3_17e" - Rest tremor amplitude (Lip/Jaw)
Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off" - Rest tremor amplitude during OFF medication assessment (ordering similar as that from Column 5 to Column 9)
Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on" - Rest tremor amplitude during ON medication assessment
For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:
Goetz, C. G. et al. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23, 2129-2170, doi:10.1002/mds.22340 (2008)
The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion.
In this dataset, there is a total of 45 files.
9 raw readings of the nine test trials (text files (.txt)) (e.g., raw_yaw_slow.txt)
Each trial contained 22 columns of data. Each column of data contains the following.
1) Sampled Time (ms)
2) Encoder (deg)
3,4,5,6) IMU 2 quaternion from DMP (4 values = a,b,c,d)
7,8,9,10) IMU 1 quaternion from DMP (4 values = a,b,c,d)
11,12,13) Raw Gyroscope Readings (deg/s) of IMU 2 about x,y,z (3 values)
14,15,16) Raw Accelerometer Readings (g - gravitational constant) of IMU 2 about x,y,z (3 values)
17,18,19) Raw Gyroscope Readings (deg/s) of IMU 1 about x,y,z (3 values)
20,21,22) Raw Accelerometer Readings (g - gravitational constant) of IMU 1 about x,y,z (3 values)
MATLAB scripts are used to process the raw text files. See the documentation for MATLAB scripts"README_MATLAB_Files.txt"). Additional 36 .mat files will be created. Description for these .mat files are given below.
9 raw readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat)
These files are similar to the previous raw text files but converted into MATLAB data space for easier processing in MATLAB.
These contain similar information as the previous raw text files as well as the line of when the calibration phase and data phase starts.
9 filtered readings of the nine test trails in MATLAB data files (.mat) (e.g., filtered_yaw_slow.mat)
These files contain filtered readings of the previous raw MATLAB files. Any missing data strings are removed. A 4th order Butterworth low-pass filter at 4 Hz is applied to the raw IMU data.
9 angle readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat) (e.g., angle_yaw_slow.mat)
These files contain computed angles from the previous filtered MATLAB files. Five computational algorithms (Accelerometer Inclination, Gyroscopic Integration, Complementary Filter, Kalman Filter, and Digital Motion Processing) are used. For more info on these algorithms, see https://github.com/ssong47/get_joint_angles_using_imus
9 metric readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat) (e.g., metric_yaw_slow.mat)
These files contain computed metrics such as Root-Mean-Squared-Errors (RMSE) for each computed algorithm. The RMSE is a metric for how accurately each algorithm can estimate the true rotated angle. A RMSE below 6 degrees is acceptable for quantifying human joint angles in biomechanics.
This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
- M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
- E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).
Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.