UaBMA-OLPP: A Novel Manifold-Inspired Technique for sEMG-Based Hand Movement and Object Grasp Recognition.

Hand-related impairments from stroke and neuromuscular diseases are rising globally, increasing the demand for prosthetic hands using surface electromyography (sEMG) for hand movement and grasp activities. sEMG systems also support human-robot interaction (HRI), aiding assistive robots in manipulator tasks. However, sEMG sensors often struggle to capture comprehensive muscle activity and are vulnerable to external hazards. In such scenarios, classical dimensionality reduction techniques often underperform, particularly when the intrinsic spatial structure of the data is overlooked. Addressing this limitation, local manifold-inspired learning techniques, such as locality preserving projection (LPP), are investigated in this study. In particular, orthogonal LPP (OLPP) helps extracting features from high-dimensional data by leveraging the orthogonal properties of nonlinear mappings from input to feature space. Nevertheless, traditional projection kernels are typically constructed based on Euclidean similarity between data points, making them highly sensitive to noise and outliers. To overcome this challenge, we have incorporated two additional similarity measures derived from the complex Euler space and Grassmannian manifold, which effectively explore the local structure in Riemannian spaces. A novel uncertainty-aware Bayesian model averaging (UaBMA) approach is proposed for integrating similarity weights from various manifolds, thereby enhancing the projection discriminability. Extensive experimental studies on both laboratory-acquired and benchmark NinaPro datasets demonstrate superior performance of the proposed technique over existing methods.