Wearable Sensing

Real-time Body Tracking


The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.

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Citation Author(s):
Submitted by:
Polona Caserman
Last updated:
Fri, 01/19/2018 - 06:09
DOI:
10.21227/H2GQ0X
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[1] , "Real-time Body Tracking", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2GQ0X. Accessed: Feb. 20, 2018.
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doi = {10.21227/H2GQ0X},
url = {http://dx.doi.org/10.21227/H2GQ0X},
author = { },
publisher = {IEEE Dataport},
title = {Real-time Body Tracking},
year = {2018} }
TY - DATA
T1 - Real-time Body Tracking
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2GQ0X
ER -
. (2018). Real-time Body Tracking. IEEE Dataport. http://dx.doi.org/10.21227/H2GQ0X
, 2018. Real-time Body Tracking. Available at: http://dx.doi.org/10.21227/H2GQ0X.
. (2018). "Real-time Body Tracking." Web.
1. . Real-time Body Tracking [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2GQ0X
. "Real-time Body Tracking." doi: 10.21227/H2GQ0X

Cellular Wireless Energy Harvesting for Smart Contact Lens Applications


An energy harvester for a smart contact lens that monitors the glucose level of a user is developed and demonstrated. The energy harvester captures a smartphone’s 2G cellular emission, and rectifies it into DC power to operate on-lens microelectronics for glucose detection and wireless data transmission. The energy harvester can reach a maximum Ra- dio Frequency (RF) to Direct Current (DC) power conversion efficiency of 47%. An electrically realistic human eye model is designed and fabricated using 3D printing technologies to assist in various measurements of the proposed energy harvester.

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Citation Author(s):
Submitted by:
luyao chen
Last updated:
Sun, 01/14/2018 - 11:03
DOI:
10.21227/H22M1J
 
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[1] , "Cellular Wireless Energy Harvesting for Smart Contact Lens Applications", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H22M1J. Accessed: Feb. 20, 2018.
@data{h22m1j-18,
doi = {10.21227/H22M1J},
url = {http://dx.doi.org/10.21227/H22M1J},
author = { },
publisher = {IEEE Dataport},
title = {Cellular Wireless Energy Harvesting for Smart Contact Lens Applications},
year = {2018} }
TY - DATA
T1 - Cellular Wireless Energy Harvesting for Smart Contact Lens Applications
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H22M1J
ER -
. (2018). Cellular Wireless Energy Harvesting for Smart Contact Lens Applications. IEEE Dataport. http://dx.doi.org/10.21227/H22M1J
, 2018. Cellular Wireless Energy Harvesting for Smart Contact Lens Applications. Available at: http://dx.doi.org/10.21227/H22M1J.
. (2018). "Cellular Wireless Energy Harvesting for Smart Contact Lens Applications." Web.
1. . Cellular Wireless Energy Harvesting for Smart Contact Lens Applications [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H22M1J
. "Cellular Wireless Energy Harvesting for Smart Contact Lens Applications." doi: 10.21227/H22M1J

Wi-Fi signal strength measurements from smartphone for various hand gestures


The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value

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Dataset Details

Citation Author(s):
Submitted by:
Ramviyas Parasuraman
Last updated:
Mon, 01/08/2018 - 21:53
DOI:
10.21227/H2C362
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[1] , "Wi-Fi signal strength measurements from smartphone for various hand gestures", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2C362. Accessed: Feb. 20, 2018.
@data{h2c362-18,
doi = {10.21227/H2C362},
url = {http://dx.doi.org/10.21227/H2C362},
author = { },
publisher = {IEEE Dataport},
title = {Wi-Fi signal strength measurements from smartphone for various hand gestures},
year = {2018} }
TY - DATA
T1 - Wi-Fi signal strength measurements from smartphone for various hand gestures
AU -
PY - 2018
PB - IEEE Dataport
UR - 10.21227/H2C362
ER -
. (2018). Wi-Fi signal strength measurements from smartphone for various hand gestures. IEEE Dataport. http://dx.doi.org/10.21227/H2C362
, 2018. Wi-Fi signal strength measurements from smartphone for various hand gestures. Available at: http://dx.doi.org/10.21227/H2C362.
. (2018). "Wi-Fi signal strength measurements from smartphone for various hand gestures." Web.
1. . Wi-Fi signal strength measurements from smartphone for various hand gestures [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2C362
. "Wi-Fi signal strength measurements from smartphone for various hand gestures." doi: 10.21227/H2C362

Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch


 

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Citation Author(s):
Submitted by:
Emanuele Ruffaldi
Last updated:
Fri, 12/22/2017 - 08:06
DOI:
10.21227/H2CD31
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[1] , "Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2CD31. Accessed: Feb. 20, 2018.
@data{h2cd31-17,
doi = {10.21227/H2CD31},
url = {http://dx.doi.org/10.21227/H2CD31},
author = { },
publisher = {IEEE Dataport},
title = {Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch},
year = {2017} }
TY - DATA
T1 - Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2CD31
ER -
. (2017). Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch. IEEE Dataport. http://dx.doi.org/10.21227/H2CD31
, 2017. Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch. Available at: http://dx.doi.org/10.21227/H2CD31.
. (2017). "Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch." Web.
1. . Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2CD31
. "Non-Resonant Kinetic Energy Harvesting using Macro-Fiber Composite Patch." doi: 10.21227/H2CD31

Mobile EEG Recordings in an Art Museum Setting


Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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Dataset Details

Citation Author(s):
Submitted by:
Justin Brantley
Last updated:
Fri, 12/22/2017 - 11:40
DOI:
10.21227/H2TM00
Data Format:
Links:
 
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[1] , "Mobile EEG Recordings in an Art Museum Setting", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2TM00. Accessed: Feb. 20, 2018.
@data{h2tm00-17,
doi = {10.21227/H2TM00},
url = {http://dx.doi.org/10.21227/H2TM00},
author = { },
publisher = {IEEE Dataport},
title = {Mobile EEG Recordings in an Art Museum Setting},
year = {2017} }
TY - DATA
T1 - Mobile EEG Recordings in an Art Museum Setting
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2TM00
ER -
. (2017). Mobile EEG Recordings in an Art Museum Setting. IEEE Dataport. http://dx.doi.org/10.21227/H2TM00
, 2017. Mobile EEG Recordings in an Art Museum Setting. Available at: http://dx.doi.org/10.21227/H2TM00.
. (2017). "Mobile EEG Recordings in an Art Museum Setting." Web.
1. . Mobile EEG Recordings in an Art Museum Setting [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2TM00
. "Mobile EEG Recordings in an Art Museum Setting." doi: 10.21227/H2TM00

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: Feb. 20, 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

TST Footwear-Based dataset for Fall Detection (TST FB4FD)


The TST FB4FD dataset contains data acquired through a pair of smart shoes. The smart shoes are specifically designed for fall detection purposes and are equipped respectively with 3 Force Sensing Resistors (FSRs) and an inertial unit.  More specifically, the dataset consists of 32 different falls and 8 activities of daily living (ADLs) performed by 17 healthy subjects aged between 21 and 55 years, for a total of 544 falls and 136 ADLs sequences .

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Dataset Details

Citation Author(s):
Submitted by:
Laura Montanini
Last updated:
Tue, 04/11/2017 - 11:11
DOI:
10.21227/H2W01S
Data Format:
Links:
 
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[1] , "TST Footwear-Based dataset for Fall Detection (TST FB4FD)", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H2W01S. Accessed: Feb. 20, 2018.
@data{h2w01s-17,
doi = {10.21227/H2W01S},
url = {http://dx.doi.org/10.21227/H2W01S},
author = { },
publisher = {IEEE Dataport},
title = {TST Footwear-Based dataset for Fall Detection (TST FB4FD)},
year = {2017} }
TY - DATA
T1 - TST Footwear-Based dataset for Fall Detection (TST FB4FD)
AU -
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H2W01S
ER -
. (2017). TST Footwear-Based dataset for Fall Detection (TST FB4FD). IEEE Dataport. http://dx.doi.org/10.21227/H2W01S
, 2017. TST Footwear-Based dataset for Fall Detection (TST FB4FD). Available at: http://dx.doi.org/10.21227/H2W01S.
. (2017). "TST Footwear-Based dataset for Fall Detection (TST FB4FD)." Web.
1. . TST Footwear-Based dataset for Fall Detection (TST FB4FD) [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H2W01S
. "TST Footwear-Based dataset for Fall Detection (TST FB4FD)." doi: 10.21227/H2W01S