The data acquisition process begins with capturing EEG signals from 20 healthy skilled volunteers who gave their written consent before performing the experiments. Each volunteer was asked to repeat an experiment for 10 times at different frequencies; each experiment was trigger by a visual stimulus.

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Mother’s Significant Feature (MSF) Dataset has been designed to provide data to researchers working towards woman and child health betterment. MSF dataset records are collected from the Mumbai metropolitan region in Maharashtra, India. Women were interviewed just after childbirth between February 2018 to March 2021. MSF comprise of 450 records with a total of 130 attributes consisting of mother’s features, father’s features and health outcomes. A detailed dataset is created to understand the mother’s features spread across three phases of her reproductive age i.e.

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We have provided the copy of forms used to collect data for datset and a read me guide to undertand the features provided in dataset along with the content of all the 6 dataset submitted in excel sheet format.

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A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.

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Participant Recruitment

Potential participants were informed of the study protocol prior to being enrolled. To be included in the study, subjects had to be over the age of 18 and under the age of 90. Informed consent was obtained from all participants. Personal data such as age, gender, height, and weight were collected prior to data collection and this information, along with collected sensor readings, was deidentified and stored in conformation with HIPAA.

Data Collection System

Blumio has conducted previous studies measuring arterial pulsations at the radial artery with millimeter-wave FMCW radar [1]. For this study, the developmental stage BGT60TR24B FMCW system (Infineon Technologies AG, Munich, Germany) was worn over the left wrist.

The data collection system also included the CNAP Monitor 500 (CNSystems Medizintechnik GmbH, Graz, Austria) worn on the left arm, a SPT-301 applanation tonometer (Millar Inc, Houston, USA) worn on the right wrist, and a SS4LA PPG transducer (BIOPAC Systems Inc, Goleta, USA) worn on the right hand’s middle digit.

Data Collection Procedures

Study protocol was approved by Western IRB prior to participant recruitment (Western IRB #20193057). All measurements were collected at the Blumio Office in San Mateo, CA. Measurements were performed according to a fixed protocol. Participants were seated at an appropriate height with both arms resting comfortably on a table in front of them. They were asked to rest quietly for 5 minutes in that position. Then, signals from the sensors were recorded simultaneously for a period of 10 minutes. During the signal acquisition period, the participant was asked to maintain a normal breathing frequency and to not speak or move.

Signal Processing

Following collection, the signals were first time-synchronized and then processed according to the steps described below.

The raw IF radar data output was processed utilizing two approaches. First, a standard phase transformation was used. This consisted of performing a Fast Fourier Transform (FFT) on the IF signal and extracting the phase from the appropriate range bin as described in our previous work. Secondly, a proprietary transformation created by Blumio was utilized. The algorithms employ a set of pre-processing and noise-reduction procedures, during which the radar signal is transformed into a univariate pulse waveform.

The auxiliary signals and the reference blood pressure data was extracted from the MP36R unit using the companion AcqKnowledge software (BIOPAC Systems Inc, Goleta, USA).

Dataset Description and Usage Notes

The entire dataset and associated participant health information are freely available for download as a ZIP file. All the sensor data is stored in CSV format. Each CSV file is named after the participant’s assigned identifier. The first column of the CSV contains the timestamp in seconds. For the sake of data analysis, all sensor channels have been time aligned in the included files. The second column includes the reference blood pressure in mmHg from the CNIBP monitor. The third column is data from the PPG sensor in mV. The fourth column includes the is the data from the applanation tonometer also in mV. The fifth column is the output from Blumio’s proprietary radar transform algorithm in arbitrary units. The sixth column is the output from the phase radar transformation algorithm in radians. Note that each file varies in length of time. Certain files have a truncated start due to the CNAP Monitor 500’s initialization period.

The included participant health information is available in a XSLX summary sheet. The information in the XSLX sheet is tabulated by participant study identifier.

Acknowledgment

The authors would like to thank the Silicon Valley Innovation Center (SVIC) and the Power & Sensor Systems (PSS) teams at Infineon Technologies AG for providing engineering support during our R&D process.

Funding

This work was supported by the Center for Disease Control under grant number 9679554 and Infineon Technologies AG.

References

[1] J. Johnson, C. Kim, and O. Shay, "Arterial Pulse Measurement with Wearable Millimeter Wave Device," in IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2019, pp. 1-4.

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Real-time gesture recognition with bio-impedance measurement. Two videos , one for hand gesture, another for pinch gesture

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It has been suggested that the wireless network evolution to smaller carrier wavelengths (from 2G to 5G) increases radio-frequency electromagnetic field (RF-EMF) absorption in Western Honey Bees (Apis mellifera). It is unknown whether the radiation performance of antennas is stable when an insect appears in their vicinity. In this research, the absorbed power in a worker honey bee and the influence of the bee's presence on antennas' radiation performance is investigated for the newly used frequencies in 5G networks, from 6-240 GHz.

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The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model.

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Biomolecular structure data analyzed in "Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm" by Wanli Qiao and Amarda Shehu.

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Silk fibroin is the structural fiber of the silk filament and it is usually separated from the external fibroin by a chemical process called degumming. This process consists in an alkali bath in which the silk cocoons are boiled for a determined time. It is also known that the degumming process impacts the property of the outcoming silk fibroin fibers.

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The data contained in the first sheet of the dataset is in tidy format (each row correspond to an observation) and can be directly imported in R and elaborated with the package Tidyverse. It should be noticed that the row with the standard order 49 correspond to the reference degumming while the row 50 correspond to the test made on the bare silk fiber (not degummed). In this last case neither the mass loss nor the secondary structures were determined. In fact, being not degummed the sericine was surrounding the fiber so the examination of the secondary structure could not be done. The first two column of the dataset represent the Standard order (the standard order in which the Design of Experiment data are elaborated) and the Run order (the randomized order in whcih the trials were performed). The next four columns are the Studied factors while the rest of the dataset reports the process yields (in this case, the properties of the outcoming silk fibers). 

The second sheet contains the information of the molecular weight of the tested samples. In this case only one sample for each triplicate was tested. Both the standard order and the run order referred to the same samples of the first sheet.

In the Raw data.zip file the raw mechanical curves are reported in OriginLab format  divided in datasheets numbered as the sample from 1 to 48 with the additions of a datasheet for the reference curves obtained form the Rokwood protocol and the curves form the raw cocoons. 

In the same archive a file with the GPC curves and their elaborations for the tested samples are reported. 

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Feature tables and source code for Camargo et al. A Machine Learning Strategy for Locomotion Classification and Parameter Estimation using Fusion of Wearable Sensors. Transactions on Biomedical Engineering. 2021

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The feature tables used for this paper can be found in ‘Classification.zip’ and ‘Regression.zip’, while source code is found in ‘CombinedLocClassAndParamEst-sourcecode.zip’. To get started, download all the files into a single folder and unzip them. Within ‘CombinedLocClassAndParamEst-master’, the folder ‘sf_analysis’ contains the main code to run, split into ‘Classification’ and ‘Regression’ code folders. There is also a 'README.md' file within the source code with more information and dependencies. If you’d like to just regenerate plots and results from the paper, then move all contents of the ‘zz_results_published’ folders (found under the feature table folders) up one folder so they are just within the ‘Classification’ or ‘Regression’ data folders. Go into the source code, find the ‘analysis’ folders, and run any ‘analyze*.m’ script with updated ‘datapath’ variables to point to the results folders you just moved.

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This data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.

The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).

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1. Download a .zip file.

2. Unzip the file and extract it into a folder. 

3. There will be two folders, namely, VCF and CADD_Scores. These folders contain the compressed .vcf and .tsv files. The .vcf files are filtered VCF files produced by the GATK best practice workflow for RNA-seq data. The reference genome hg19 was used. There is also a .xlsx file containing the run accession IDs (e.g., SRR12095153) and URLs (e.g., https://www.ebi.ac.uk/ena/browser/view/SRR12095153) from where the paired end sequences were downloaded. Complete description of the sequences can be found via these URLs.

4. Check for new .zip files.

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Human Neck movements data acquired using Meatwear - CPRO device - Accelerometer-based Kinematic data. Data fed to OpenSim simulation software extracted Kinematics and Kinetics (Muscles, joints - Forces, Acceleration, Position)

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