Title:
A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities
A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities
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Author(s)
Scherpereel, Keaton
Molinaro, Dean
Inan, Omer
Shepherd, Maxwell
Young, Aaron
Molinaro, Dean
Inan, Omer
Shepherd, Maxwell
Young, Aaron
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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.
Sponsor
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.
Date Issued
2023
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Attribution 4.0 International