Biomedical and Health Sciences

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ISRMyo-I


The published sEMG database was captured by the Intelligent System and Biomedical Robotics Group at University of Portsmouth, leaded by Prof. Honghai Liu.

 

Six subjects were volunteered for data capturing, and the sEMG data were captured in ten separate days. We manually separated the whole database into two parts: training dataset (the first 7 days) and testing dataset(the last 3 days). For each subject, two folders exist, one for training and the other for test. 

 

License: Creative Commons Attribution

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Citation Author(s):
Submitted by:
Yinfeng Fang
Last updated:
Tue, 03/27/2018 - 19:47
DOI:
10.21227/H28364
 
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[1] , "ISRMyo-I", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H28364. Accessed: Jul. 21, 2018.
@data{h28364-18,
doi = {10.21227/H28364},
url = {http://dx.doi.org/10.21227/H28364},
author = { },
publisher = {IEEE Dataport},
title = {ISRMyo-I},
year = {2018} }
TY - DATA
T1 - ISRMyo-I
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H28364
ER -
. (2018). ISRMyo-I. IEEE Dataport. http://dx.doi.org/10.21227/H28364
, 2018. ISRMyo-I. Available at: http://dx.doi.org/10.21227/H28364.
. (2018). "ISRMyo-I." Web.
1. . ISRMyo-I [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H28364
. "ISRMyo-I." doi: 10.21227/H28364

Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection


TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken.

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Dataset Details

Citation Author(s):
Submitted by:
SANA FATIMA
Last updated:
Mon, 02/26/2018 - 09:06
DOI:
10.21227/H2VD3C
Data Format:
 
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[1] , "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2VD3C. Accessed: Jul. 21, 2018.
@data{h2vd3c-18,
doi = {10.21227/H2VD3C},
url = {http://dx.doi.org/10.21227/H2VD3C},
author = { },
publisher = {IEEE Dataport},
title = {Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection},
year = {2018} }
TY - DATA
T1 - Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2VD3C
ER -
. (2018). Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. IEEE Dataport. http://dx.doi.org/10.21227/H2VD3C
, 2018. Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. Available at: http://dx.doi.org/10.21227/H2VD3C.
. (2018). "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." Web.
1. . Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2VD3C
. "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." doi: 10.21227/H2VD3C

Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection


TB (Tuberculosis) is a contagious disease which is caused by a bacterium named Mycobacterium Tuberculosis. Screening is done to confirm the presence of TB using different screening techniques available i.e. Chest X-ray, Microscopy, Gene Xpert and Culture etc. Medical image processing is a rapidly growing field of image processing that is used to automate different medical procedures. In this research we have designed two automated systems for the screening of TB patients. A sample of 50 images for microscopy slides and chest X-ray radiographs were taken.

License: Creative Commons Attribution

Dataset Files

No Data files have been uploaded.

Dataset Details

Citation Author(s):
Submitted by:
SANA FATIMA
Last updated:
Mon, 02/26/2018 - 08:13
DOI:
10.21227/H2036N
Data Format:
 
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[1] , "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2036N. Accessed: Jul. 21, 2018.
@data{h2036n-18,
doi = {10.21227/H2036N},
url = {http://dx.doi.org/10.21227/H2036N},
author = { },
publisher = {IEEE Dataport},
title = {Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection},
year = {2018} }
TY - DATA
T1 - Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2036N
ER -
. (2018). Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. IEEE Dataport. http://dx.doi.org/10.21227/H2036N
, 2018. Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection. Available at: http://dx.doi.org/10.21227/H2036N.
. (2018). "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." Web.
1. . Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2036N
. "Automation and Analysis of Chest X-ray and Microscopy images for tuberculosis detection." doi: 10.21227/H2036N

Investigating Velopharyngeal Closure Force with Linear Regression


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License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Anish Sana
Last updated:
Tue, 02/20/2018 - 11:37
DOI:
10.21227/H2F95C
 
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[1] , "Investigating Velopharyngeal Closure Force with Linear Regression", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2F95C. Accessed: Jul. 21, 2018.
@data{h2f95c-18,
doi = {10.21227/H2F95C},
url = {http://dx.doi.org/10.21227/H2F95C},
author = { },
publisher = {IEEE Dataport},
title = {Investigating Velopharyngeal Closure Force with Linear Regression},
year = {2018} }
TY - DATA
T1 - Investigating Velopharyngeal Closure Force with Linear Regression
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2F95C
ER -
. (2018). Investigating Velopharyngeal Closure Force with Linear Regression. IEEE Dataport. http://dx.doi.org/10.21227/H2F95C
, 2018. Investigating Velopharyngeal Closure Force with Linear Regression. Available at: http://dx.doi.org/10.21227/H2F95C.
. (2018). "Investigating Velopharyngeal Closure Force with Linear Regression." Web.
1. . Investigating Velopharyngeal Closure Force with Linear Regression [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2F95C
. "Investigating Velopharyngeal Closure Force with Linear Regression." doi: 10.21227/H2F95C

Activities of Daily Living


Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living). 

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Dataset Details

Citation Author(s):
Submitted by:
Walid Gomaa
Last updated:
Wed, 11/22/2017 - 09:06
DOI:
10.21227/H2PS74
Data Format:
 
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[1] , "Activities of Daily Living", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2PS74. Accessed: Jul. 21, 2018.
@data{h2ps74-17,
doi = {10.21227/H2PS74},
url = {http://dx.doi.org/10.21227/H2PS74},
author = { },
publisher = {IEEE Dataport},
title = {Activities of Daily Living},
year = {2017} }
TY - DATA
T1 - Activities of Daily Living
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2PS74
ER -
. (2017). Activities of Daily Living. IEEE Dataport. http://dx.doi.org/10.21227/H2PS74
, 2017. Activities of Daily Living. Available at: http://dx.doi.org/10.21227/H2PS74.
. (2017). "Activities of Daily Living." Web.
1. . Activities of Daily Living [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2PS74
. "Activities of Daily Living." doi: 10.21227/H2PS74

SUT-Lips-DB - A database of lips traces


A database of lips traces
Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip prints are unique and permanent for each individual, and next to the fingerprinting, dental identification, and DNA analysis can be one of the basis for criminal/forensics analysis.

License: Creative Commons Attribution

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OPEN ACCESS Dataset Details

Citation Author(s):
Submitted by:
Dariusz Mrozek
Last updated:
Sat, 06/16/2018 - 23:18
DOI:
10.21227/H2R04P
Data Format:
Links:
 
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[1] , "SUT-Lips-DB - A database of lips traces", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2R04P. Accessed: Jul. 21, 2018.
@data{h2r04p-17,
doi = {10.21227/H2R04P},
url = {http://dx.doi.org/10.21227/H2R04P},
author = { },
publisher = {IEEE Dataport},
title = {SUT-Lips-DB - A database of lips traces},
year = {2017} }
TY - DATA
T1 - SUT-Lips-DB - A database of lips traces
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2R04P
ER -
. (2017). SUT-Lips-DB - A database of lips traces. IEEE Dataport. http://dx.doi.org/10.21227/H2R04P
, 2017. SUT-Lips-DB - A database of lips traces. Available at: http://dx.doi.org/10.21227/H2R04P.
. (2017). "SUT-Lips-DB - A database of lips traces." Web.
1. . SUT-Lips-DB - A database of lips traces [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2R04P
. "SUT-Lips-DB - A database of lips traces." doi: 10.21227/H2R04P

Health Timeline Insight-based Dataset


Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Andres Ledesma
Last updated:
Mon, 10/16/2017 - 07:30
DOI:
10.21227/H2KD14
Data Format:
 
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[1] , "Health Timeline Insight-based Dataset", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2KD14. Accessed: Jul. 21, 2018.
@data{h2kd14-17,
doi = {10.21227/H2KD14},
url = {http://dx.doi.org/10.21227/H2KD14},
author = { },
publisher = {IEEE Dataport},
title = {Health Timeline Insight-based Dataset},
year = {2017} }
TY - DATA
T1 - Health Timeline Insight-based Dataset
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2KD14
ER -
. (2017). Health Timeline Insight-based Dataset. IEEE Dataport. http://dx.doi.org/10.21227/H2KD14
, 2017. Health Timeline Insight-based Dataset. Available at: http://dx.doi.org/10.21227/H2KD14.
. (2017). "Health Timeline Insight-based Dataset." Web.
1. . Health Timeline Insight-based Dataset [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2KD14
. "Health Timeline Insight-based Dataset." doi: 10.21227/H2KD14

BHI 2017: Big Data Analytics Competition


Data Science is all about the processes and methods to access and analyze data to gain insights for informed decision making. To promote the awareness and analytic technology of Big Data, IEEE EMBS and the IEEE Big Data Initiative are organizing a Data Analytics Competition. The competition will be held during the International Conference on Biomedical and Health Informatics (IEEE BHI2017), 16-19 February 2017 in Orlando, Florida, and is open to all participants of the conference.


The submission period for this data competition has ended.

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Dataset Details

Citation Author(s):
Submitted by:
Chris Franzino
Last updated:
Tue, 08/08/2017 - 10:52
DOI:
10.21227/H2W886
Data Format:
Links:
 
Cite

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[1] , "BHI 2017: Big Data Analytics Competition", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2W886. Accessed: Jul. 21, 2018.
@data{h2w886-16,
doi = {10.21227/H2W886},
url = {http://dx.doi.org/10.21227/H2W886},
author = { },
publisher = {IEEE Dataport},
title = {BHI 2017: Big Data Analytics Competition},
year = {2016} }
TY - DATA
T1 - BHI 2017: Big Data Analytics Competition
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H2W886
ER -
. (2016). BHI 2017: Big Data Analytics Competition. IEEE Dataport. http://dx.doi.org/10.21227/H2W886
, 2016. BHI 2017: Big Data Analytics Competition. Available at: http://dx.doi.org/10.21227/H2W886.
. (2016). "BHI 2017: Big Data Analytics Competition." Web.
1. . BHI 2017: Big Data Analytics Competition [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2W886
. "BHI 2017: Big Data Analytics Competition." doi: 10.21227/H2W886

TST Intake Monitoring dataset v2


The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Susanna Spinsante
Last updated:
Fri, 01/06/2017 - 16:44
DOI:
10.21227/H2BC7W
Data Format:
Links:
 
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[1] , "TST Intake Monitoring dataset v2 ", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2BC7W. Accessed: Jul. 21, 2018.
@data{h2bc7w-16,
doi = {10.21227/H2BC7W},
url = {http://dx.doi.org/10.21227/H2BC7W},
author = { },
publisher = {IEEE Dataport},
title = {TST Intake Monitoring dataset v2 },
year = {2016} }
TY - DATA
T1 - TST Intake Monitoring dataset v2
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H2BC7W
ER -
. (2016). TST Intake Monitoring dataset v2 . IEEE Dataport. http://dx.doi.org/10.21227/H2BC7W
, 2016. TST Intake Monitoring dataset v2 . Available at: http://dx.doi.org/10.21227/H2BC7W.
. (2016). "TST Intake Monitoring dataset v2 ." Web.
1. . TST Intake Monitoring dataset v2 [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2BC7W
. "TST Intake Monitoring dataset v2 ." doi: 10.21227/H2BC7W

TST Intake Monitoring dataset v1


The dataset contains depth frames collected using Microsoft Kinect v1 during the execution of food and drink intake movements.

License: Creative Commons Attribution

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Dataset Details

Citation Author(s):
Submitted by:
Susanna Spinsante
Last updated:
Fri, 01/06/2017 - 16:19
DOI:
10.21227/H2G59T
Data Format:
Links:
 
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[1] , "TST Intake Monitoring dataset v1 ", IEEE Dataport, 2016. [Online]. Available: http://dx.doi.org/10.21227/H2G59T. Accessed: Jul. 21, 2018.
@data{h2g59t-16,
doi = {10.21227/H2G59T},
url = {http://dx.doi.org/10.21227/H2G59T},
author = { },
publisher = {IEEE Dataport},
title = {TST Intake Monitoring dataset v1 },
year = {2016} }
TY - DATA
T1 - TST Intake Monitoring dataset v1
AU -
PY - 2016
PB - IEEE Dataport
UR - 10.21227/H2G59T
ER -
. (2016). TST Intake Monitoring dataset v1 . IEEE Dataport. http://dx.doi.org/10.21227/H2G59T
, 2016. TST Intake Monitoring dataset v1 . Available at: http://dx.doi.org/10.21227/H2G59T.
. (2016). "TST Intake Monitoring dataset v1 ." Web.
1. . TST Intake Monitoring dataset v1 [Internet]. IEEE Dataport; 2016. Available from : http://dx.doi.org/10.21227/H2G59T
. "TST Intake Monitoring dataset v1 ." doi: 10.21227/H2G59T

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