Data-Mined Continuous Hip-Knee Coordination Mapping With Motion Lag for Lower-Limb Prosthesis Control

Xu, Jian; Fang, Hongbin; Zhang, Xiaoxu; Wang, Qining

doi:10.1109/tnsre.2022.3179978

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…In 2022, Lv et al [19] focused on knee joint trajectory planning for lower limb prostheses, introducing a novel approach through experimental data mining. Coordination indexes, including mean absolute relative phase (MARP) and deviation phase, reveal a steady stage variance between hip and knee motions.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

S. Prakash

2024

jes

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Activity recognition plays pivotal role in enhancing functionality for prosthetic devices, ensuring seamless integration with users' movements. However, the complexity arises from the diverse data sources, including acceleration, angular velocity, joint angles, orientation, electromyography (EMG), and marker data, necessitating a robust approach to overcome challenges in information integration. The primary challenge lies in the effective utilization of multiple sensor modalities, each with unique characteristics and potential noise sources. The proposed solution addresses this by employing advanced sensor fusion techniques, such as Kalman filtering, during data collection. Synchronization and resampling ensure temporal consistency, while noise reduction techniques, such as low-pass filters, mitigate signal distortions. To further refine the process, a hybrid optimization-based feature selection Adaptive Step Size in Marine Predators Algorithm (ASSMPA) is introduced, focusing on marker data features. ASSMPA synergizes Marine Predators Algorithm (MPA) and Pathfinder Algorithm (PFA) for optimal feature selection in marine predator pathfinding tasks. The Feature Fusion step integrates attention mechanisms to dynamically weigh the significance of different sensor modalities during the fusion process. This strategic fusion enhances the overall performance of the Multi-Modal Hierarchical Neural Network (MMHNN). The proposed model is implemented using Python.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

S. Prakash

2024

jes

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“…Furthermore, Minjae [125] mapped the autonomous motion of the residual limb (thigh) to the impedance parameters of the prosthesis controller, thus achieving level walking under three stride lengths. The extended work was proposed to achieve the trajectory planning of the knee joint [126]. Also, nonlinear autoregressive networks have been proposed to avoid control switching at different walking speeds [127].…”

Section: A Complete Coordination On Structured Terrainsmentioning

confidence: 99%

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

Ma,

Zhang,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Gait coordination (GC), meaning that one leg moves in the same pattern but with a specific phase lag to the other, is a spontaneous behavior in the walking of a healthy person. It is also crucial for unilateral amputees with the robotic leg prosthesis to perform ambulation cooperatively in the real world. However, achieving the GC for amputees poses significant challenges to the prostheses' dynamic modeling and control design. Still, there has not been a clear survey on the initiation and evolution of the detailed solutions, hindering the precise decision of future explorations. To this end, this paper comprehensively reviews GCoriented dynamic modeling and adaptive control methods for robotic leg prostheses. Considering the two representative environments concerned with adaptive control, we first classify the dynamic models into the deterministic model for structured terrain and the constrained stochastic model for stochastically uneven terrain. Inspired by the concept of synchronization, we then emphasize three typical problems for the GC realization, i.e., complete coordination on structured terrain, stochastic coordination on stochastically uneven terrain, and finite-time delayed stochastic coordination. Finally, we conclude with a discussion on the remaining challenges and opportunities in controlling robotic leg prostheses.

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“…Moreover, finite-state-based control strategies inevitably introduce many device parameters, thresholds, and switching rules to refine the division of the gait phases [ 12 , 14 ]. Au et al [ 15 ] conducted experiments to establish device settings and mode transition rules for different gait phase stages.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes

Ding,

Georgilas,

Plummer

2023

Sensors

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Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired t-tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired t-test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns.

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Data-Mined Continuous Hip-Knee Coordination Mapping With Motion Lag for Lower-Limb Prosthesis Control

Cited by 6 publications

References 32 publications

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes

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