A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities

Scherpereel, Keaton; Molinaro, Dean; Inan, Omer; Shepherd, Maxwell; Young, Aaron

Title:

A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities

dc.contributor.author	Scherpereel, Keaton
dc.contributor.author	Molinaro, Dean
dc.contributor.author	Inan, Omer
dc.contributor.author	Shepherd, Maxwell
dc.contributor.author	Young, Aaron
dc.contributor.corporatename	Georgia Institute of Technology. Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab	en_US
dc.contributor.corporatename	Georgia Institute of Technology. George W. Woodruff School of Mechanical Engineering	en_US
dc.date.accessioned	2023-03-01T21:54:48Z
dc.date.accessioned	2023-03-02T18:14:55Z
dc.date.available	2023-03-01T21:54:48Z
dc.date.available	2023-03-02T18:14:55Z
dc.date.issued	2023
dc.description.abstract	Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and EMG were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.	en_US
dc.description.sponsorship	This material is based upon work supported in part by the National Science Foundation Graduate Research Fellowship under Grant No. (DGE-2039655) and in part by X, The Moonshot Factory . This work was also supported in part by the NSF FRR program through award #2233164 and #2328051.	en_US
dc.identifier.uri	http://hdl.handle.net/1853/70296
dc.identifier.uri	https://doi.org/10.35090/gatech/70296
dc.publisher	Georgia Institute of Technology	en_US
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Biomechanics	en_US
dc.subject	Noncyclic	en_US
dc.subject	Human motion	en_US
dc.subject	EMG	en_US
dc.subject	Human kinetics	en_US
dc.subject	Human kinematics	en_US
dc.title	A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities	en_US
dc.title.alternative	A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities	en_US
dc.type	Dataset	en_US
dspace.entity.type	Publication
local.contributor.author	Young, Aaron
local.contributor.corporatename	George W. Woodruff School of Mechanical Engineering
local.contributor.corporatename	College of Engineering
local.contributor.corporatename	Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab
relation.isAuthorOfPublication	7f9a67d3-b78f-45e2-a5e9-d9a1650849db
relation.isOrgUnitOfPublication	c01ff908-c25f-439b-bf10-a074ed886bb7
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication	bd9d5cae-3cf4-4aea-ae25-8860131dd14d