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

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 multicontext, intent recognition system that was deployed in real-time on an Open Source Leg (OSL). We recruited 11 individuals with transfemoral amputation, with seven 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, and (2) IND walking speed estimates showed ∼0.09 m/s mean absolute error (MAE) and slope estimates showed ∼0.95 deg MAE across multicontext 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.