A Comparative Study of sEMG Gesture Recognition Algorithms Based on Ninapro DB1

Systematic Evaluation of sEMG Processing Pipelines for Gesture Recognition

Gesture recognition enables intuitive human–robot communication, where surface electromyography (sEMG) provides a minimally invasive interface for detecting motor activity. However, the lack of systematic evaluation of processing pipelines represents a critical barrier to reliable subject-independent deployment. This work presents a systematic evaluation of sEMG processing pipelines for three-gesture recognition (Neutral, Ask, Take) using a Bagged Trees classifier under Leave-One-Subject-Out (LOSO) cross-validation across ten participants. A mixed-effects ANOVA over 1872 experimental configurations revealed that the preprocessing pipeline is the dominant factor affecting classification accuracy, followed by inter-subject variability, while window size and overlap exhibit smaller but statistically significant effects. A consistency-based elbow analysis identified a compact subset of five features that reduced input dimensionality by 94.79% while improving accuracy from 68.10% to 69.86% and reducing training time by 51.62%. Bayesian hyperparameter optimization was also tested, but it did not yield statistically significant improvements over the five-feature baseline; given the limited cohort (n = 10), this indicates the absence of a detectable difference and points to inter-subject variability as a leading factor limiting performance rather than model configuration. These findings provide empirically grounded guidelines for designing computationally efficient sEMG-based gesture recognition systems for collaborative robotics.

An Exploratory Study of Single Channel Surface Electromyography for Hand Gesture Classification

This study investigates the feasibility of classifying ten hand gestures using a single sEMG channel combined with lightweight machine learning architectures and demonstrates that combining time and frequency features with Pearson filtering and a compact NN can achieve up to 90 percent accuracy.

NewRep: a classification model for online pattern recognition based on HD-EMG signals.

Knowledge distillation for sEMG-based gesture recognition: enhancing wearable HMI systems with lightweight models

This research enhances the feasibility of embedding deep learning models into wearable systems (e.g. wristband or wristwatch), facilitating more responsive and efficient gesture recognition in daily-life applications.

Classification of Gesture Electromyography by Dynamic Mode Decomposition

Accurate classification of hand gestures from surface electromyography (sEMG) signals is essential for human-computer interaction and myoelectric prosthetic control, yet conventional feature-extraction methods struggle to capture the temporal dynamics of muscle activation. Dynamic Mode Decomposition (DMD), a data-driven technique originally developed for fluid dynamics, has not been extensively validated for sEMG signals. This study addresses that gap by introducing DMD as a feature-extraction framework for sEMG-based gesture classification, providing a mathematically rigorous approach for capturing spatiotemporal muscle-activation patterns. Using sEMG recordings from 37 participants, we extracted DMD-reconstructed features–Mean Absolute Value (MAV), Root Mean Square (RMS), Simple Square Integral (SSI), Variance (VAR), Standard Deviation (STD), Median, Integrated EMG (IEMG), and spectral features–and compared their discriminative power against traditional features across multiple classifiers. DMD-derived features significantly distinguished common hand gestures ( $p \lt 0.05$ , Bonferroni-corrected) and consistently outperformed traditional features: MAV, RMS, SSI, and VAR achieved 96% accuracy with k-nearest neighbors and 88% with random forest, while support vector machines reached 97% and 96% accuracy using STD, MAV, and RMS, respectively. Convolutional Neural Networks and Deep Stacked Neural Networks achieved up to 85% and 87% accuracy, respectively, using DMD-extracted spectral features. These results establish DMD as an effective, interpretable feature-extraction method for sEMG-based gesture classification, with applications in rehabilitation engineering, prosthetic control, and human-computer interaction.

A Lightweight Upper Limb Intention Recognition Method Based on sEMG-IMU Fusion and PCA

To address the demand for lightweight intention recognition algorithms in home-based portable upper limb rehabilitation robots, this paper presents a lightweight upper limb motion intention recognition scheme based on bimodal fusion of surface electromyography (sEMG) and inertial measurement unit (IMU) signals. Time-domain features are extracted from sEMG and statistical features from IMU, followed by feature-level concatenation. Principal component analysis (PCA) is then applied for dimensionality reduction, and a Support Vector Machine (SVM) is employed to classify five wrist movements. Experiments on the publicly available Ninapro DB5 dataset validate that bimodal fusion enhances recognition accuracy, while PCA effectively compresses the feature space with minimal performance degradation. Key design choices, hyperparameter configurations, and performance boundaries are reported to facilitate practical deployment on resource-constrained portable rehabilitation devices.