A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities

Autor: Keaton Scherpereel, Dean Molinaro, Omer Inan, Max Shepherd, Aaron Young
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Data, Vol 10, Iss 1, Pp 1-12 (2023)
Druh dokumentu: article
ISSN: 2052-4463
DOI: 10.1038/s41597-023-02840-6
Popis: 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 electromyography (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.
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