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:

   "AbbreviatedSensorLocation"_ID"ParticipantID"Accel.csv

   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:

   AnnotID"ParticipantID".csv

   E.g. AnnotID018.csv 

    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)   

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. 

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.  

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Author Contributions:

• 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 

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Acknowledgment

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).