The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).
Magnetic guidance of cochlear implants is a promising technique to reduce the risk of physical trauma during surgery. In this approach, a magnet attached to the tip of the implant electrode array is guided within the scala tympani using a magnetic field. After surgery, the magnet must be detached from the implant electrode array via localized heating, which may cause thermal trauma, and removed from the scala tympani .
The datasets are related to the findings and results of our investigations of the minimal force thresholds perception in robotic surgical applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system displays grasping tasks by a surgical grasper. The experiments included the display of a set of presentations in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). Sixty participants took part in these experiments and were asked to distinguish between consecutive presentations.
Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.
The PD-BioStampRC21 dataset provides data from a wearable sensoraccelerometry study conducted for studying activity, gait, tremor, andother motor symptoms in individuals with Parkinson's disease (PD). Inaddition to individuals with PD, the dataset also includes data forcontrols that also went through the same study protocol as the PDparticipants. Data were acquired using lightweight MC 10 BioStamp RCsensors (MC 10 Inc, Lexington, MA), five of which were attached toeach participant for gathering data over a roughly two dayinterval.
Users of the dataset should cite the following paper:
Adams JL, Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey E, Sharma G. "A real-world study of wearable sensors in Parkinson’s disease". NPJ Parkinson's disease. 2021 Nov 29;7(1):1-8.
An overview of the study protocol is also provided in the abovementioned paper. Additional detail specific to the dataset and filenaming conventions is provided here.
The dataset is comprised of two main components: (I) Sensor andUPDRS-assessment-task annotation data for each participant and (II)demographic and clinical assessment data for all participants. Each ofthese is described in turn below:
I) Sensor and UPDRS-assessment-task annotation data:
The sensor accelerometry and UPDRS-assessment-task annotation data forall the participants are provided as a zip file namedFullDataSet_PD-BioStampRC21.zip. The size of the zip file is 11GB and,when unzipped, it generates a set of folders and files with a totalsize of approximately 56GB. Unzipping the file generates folders withname matching the participant ID for each of the Control and PDparticipants (17 Control + 17 PD). Each participant folder containsthe data organized as the following files.
a) Accelerometer sensor data files (CSV) corresponding to the fivedifferent 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)
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).
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 areprovided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzippingthe file generates a CSV file named Clinic_Data_PD-BioStampRC21.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
Note that columns 10-19 do not contain any data for controlparticipants and for PD participants that did not participate in theON/OFF medication component of the assessment protocol for the study.
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 ofthe Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scalepresentation and clinimetric testing results. Mov Disord 23,2129-2170, doi:10.1002/mds.22340 (2008)
These .MAT files contain MATLAB Tables of raw and preprocessed data. Information detailing the bed system used to collect these signals and the steps used to create the preprocessed data are contained in a publication in Sensors – Carlson, C.; Turpin, V.-R.; Suliman, A.; Ade, C.; Warren, S.; Thompson, D.E.; Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters. Sensors 2021, 21, 156. https://doi.org/10.3390/s21010156.
The reBAP signal is scaled at 100 mmHg/volt. The interbeat interval (IBI), stroke volume (SV), and dP/dt_max are scaled at 1000 ms/volt, 100 mL/volt, and 1 mHg/s/volt, respectively.
Fig. 5. Flux density component Bz at different depths
Fig. 8. Comparison of needle temperature curves of the BP and S coils
This dataset is in support of my 3 research papers 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part I', 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part II' and 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part III'.
This dataset is in support of my 4 Research papers, initially submitted to different journals
Related Reseach Papers :
- Novel ß-Bio Model (Mathematics Foundation)
- ß-Model of (Preprint: )
- and Humans Body - Part I (Preprint: )
- and Humans Body - Part II (Preprint: )
(1) This is an open access ,so everything can be downloaded after login (free signup). You have to click on 'Title'.
(2) Data which was earlier uploaded in 2021 under this same DOI 'Electro-Magnetic Radiations and Human Body' is explained in ' Experimental Physical Recording’. That data is as it is. Neither earlier data is removed nor it is modified, it is as it was earlier submitted. No additions are even done.
(3) The main paper which has my scientific analysis on 'Electro-Magnetic Radiations and Human Body' is ‘ and Humans Body’. This paper is used as the foundation because of the accepted facts by WHO, ICNIRP, IARC, NIH,medical doctors, and biomedical engineers. In this paper, I have claimed and proved something.
(4) Zip do not contain any simulation project folder.
(5) Extra Libraries created, modified , other scripts , not shared, as very elementary for any graduate,degree holder, so only results given in research paper.
(6) For details like model block diagram, parameters, analysis, interpretation, mathematical formulae used to obtain these results etc. please refer "Research Paper".
(7) Radiation patterns - If you expecting the patterns are something easy to understand or decode, but they cannot easily interpreted. For this, pls. refer either textbook or research paper.
(8) The mobile tower installation/distance parameters are also taken according to 'Ministry of Communications, Department of Telecommunications,GoI.
(9) All operating frequency ranges are not mentioned for each 2G,3G,4G,5G,6G. For complete operating frequencies, pls refer your country or search on net. For other details, pls see Research paper.
(10) This work has undergone complete revisions, loss of data many times, and many computer crashes.
(11) This is the last version in those datasets. Only update will be related to ß-Bio models which I
(12) All work is simple , on basic and elementary concepts, can be easily copied, remade and understood.
(13) The dataset has been checked by the 'Data or Code or model Inspector' before uploading.
(14) If any problem in creating or copying, pls contact your university professor or board or any of the companies engineer.
(15) As such, No other question or email will be replied. I may have left completely R&D or other reason.
All the following 25 folders are zipped.
- 2G_800 is CDMA 800MHz or 0.8 GHz
- 2G_900 is GSM 900MHZ or 0.9 GHz
- 2G_1800 is GSM1800MHz or 1.8 GHz
- 3G_1900 is 1900 MHz or 1.9 GHz
- 3G_2100 is 2100 MHz or 2.1 GHz
- 4G_2300 is 2300 MHz or 2.3 GHz
- 4G_2400 is 2400 MHz or 2.4 GHz
- 4G_2600 is 2600 MHz or 2.6 GHz
4) Low/Mid 5G FR1
- 5G_3300 is 3300 MHz or 3.3 GHz
- 5G_3500 is 3500 MHz or 3.5 GHz
- 5G_5200 is 5200 MHz or 5.2 GHz
- 5G_5900 is 5900 MHz or 5.9 GHz
- 5G_6000 is 6000 MHz or 6 GHz
- 5G_6200 is 6200 MHz or 6.2 GHz
Here 5G_3500 is n78 C-Band but 5G_6000, 5G_6200 are TDD, n96, n102 UNII defined by US FCC. For details, pls refer Research paper.
5) High 5G FR2
- 5G_26000 is 26000 MHz or 26 GHz
- 5G_28000 is 28000 MHz or 28 GHz
- 5G_39000 is 39000 MHz or 39 GHz
- 5G_41000 is 41000 MHz or 41 GHz
- 5G_47000 is 47000 MHz or 47 GHz
- 6G_90000 is 90,000 MHz or 90 GHz
- 6G_150000 is 150 GHz
- 6G_220000 is 220 GHz
- 6G_500000 is 500 GHz
- 6G_750000 is 750 GHz
- 6G_1100000 is 1100 GHz, that is, 1.1 Terahertz (THz)
8) Each of the above zip has following datasets. The plots, images can be seen in IEEE CodeOcean DOI.
9) 3G has addition dataset
10) Following datasets are based on ß-Bio
Experimental Physical Recording
The folder 'PhysicalRecording_2021.zip ' has recordings of Magnetic fields in the year 2021 measured using Magnetic Sensor, mobile app(software) and mobile phone
- 327uT at 0:19/00:20 . At 0:19/0:20 of the recording, 327 uT reading
- 11uT at 0:04/0:05 . At 0:04/0:05 of the recording, 11 uT reading
- 5gproof.zip has screenshots from wifi detection
- 479uT at 0:42/0:44 . At 0:42/0:44 of the recording, 479 uT reading
- Physical Magnetic Sensor(hardware)
Resolution of the sensor is 0.0976 uT & Maximum Range of the sensor : 3000.0044 uT
- Physical orientation and angular velocity Sensor (hardware)
Resolution of the sensor is 0.0012216975 rad/s & Maximum Range of the sensor : 34.90549 rad/s
- Physical Proximity Sensor (hardware)
Resolution of the sensor : 1.0 cm & Maximum Range of the sensor : 5.0 cm
- Physical Gravity Sensor (hardware)
Resolution of the sensor : 0.01 m/s2 & Maximum Range of the sensor :156.98999 m/s2
Experimental Result - Lowest Recorded Reading : 11 uT
Highest Recorded Reading : 479 uT
Around 300 uT was measured anywhere, if nearby has 5G equipment ( fluctuates to 50 uT then 111, then 200 , 286, ...) . More details in paper.
Reading of 479 was measured, as few people were feeling unwell and when I checked, it was 420 uT, stationary and fluctuating to it around but that is not recorded. So after some time, this was recorded.
But later, this reading went to below 200 uT ? And even from 30 uT to 150 uT , how come
Experimental Result - 24 April 2022, See Corona cases, rising, reading which was 29uT to 150uT is 243.95 uT
For scripts of IEEE Codeocean (Rstudio & Matlab). To see colored plots and images, pls. read details given in ReadMe.txt.
- Capsule : Plots of EM Fields in 2G , DOI :
- Capsule : Plots of EM Fields in 3G , DOI :
- Capsule : Plots of EM Fields in 4G , DOI :
- Capsule : Plots of EM Fields in 5G , DOI :
- Capsule : Plots of EM Fields in 6G , DOI :
Paper Citing : If want to cite this in paper etc. ,please refer DoI and/or this url.
Funding: There are no funders for this submission. The author has himself fully self-financed (for his passion).I expect all these papers, would be nice Shroud for the passion and the price paid.
Acknowledgement : The author has generated this on Linux and had even used IEEE partner- Code Ocean - Python,C, Matlab ,Cloud Workstation, Jupyter Notebook,Rstudio,stata,julia,Tensorflow, pandas,trial (evaluation) of many proprietary softwares. No paid research, personal R&D work with no support, wastage of time in self teaching.Few gave trial (evaluation) sw with 2-5 months with even willing for 3-6 months further extension but didnt accepted hire contract request (the names cannot be disclosed & word of acknowledging expired in duration). No industry or academic will use their time only doing this work, even if given free unless financed or top MNC. The author does not have any special name to be acknowledged.
This dataset is in support of my research paper 'Comparative Non-Linear Flux Matrices & Thermal Losses in BLDC with Different Pole Pairs' .