Continuous-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses.

IF 1.7 4区 医学 Q4 BIOPHYSICS
Krishan Bhakta, Jairo Maldonado-Contreras, Jonathan Camargo, Sixu Zhou, William Compton, Kinsey Herrin, Aaron Young
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引用次数: 0

Abstract

Community ambulation is essential for maintaining a healthy lifestyle, but it poses significant challenges for individuals with limb loss due to complex task demands. In wearable robotics, particularly powered prostheses, there is a critical need to accurately estimate environmental context, such as walking speed and slope, to offer intuitive and seamless assistance during varied ambulation tasks. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time on an open-source knee and ankle powered prosthesis (OSL). We recruited 11 individuals with transfemoral amputation, with 7 participants used for real-time validation. Our findings revealed two main conclusions: 1) the user-independent (IND) performance across speed and slope was not statistically different from user-dependent (DEP) models in real-time, and did not degrade compared to its offline counterparts, 2) IND walking speed estimates showed ~0.09 m/s mean absolute error (MAE) and slope estimates showed ~0.95° MAE across multi-context scenarios. Additionally, we provide an open-source dataset to facilitate further research in accurately estimating speed and slope using an IND approach in real-world walking tasks on a powered prosthesis.

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来源期刊
CiteScore
3.40
自引率
5.90%
发文量
169
审稿时长
4-8 weeks
期刊介绍: Artificial Organs and Prostheses; Bioinstrumentation and Measurements; Bioheat Transfer; Biomaterials; Biomechanics; Bioprocess Engineering; Cellular Mechanics; Design and Control of Biological Systems; Physiological Systems.
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