The evolution of anthropomorphic robotic hands (ARH) in recent years has been sizable, employing control techniques based on machine learning classifiers for myoelectric signal processing. This work introduces an innovative multi-channel bio-signal transformer (MuCBiT) for surface electromyography (EMG) signal recognition and classification. The proposed MuCBiT is an artificial neural network based on fully connected layers and transformer architecture. The MuCBiT recognizes and classifies EMG signals sensed from electrodes patched over the arm’s surface. The MuCBiT classifier was trained and validated using a collected dataset of four hand gestures across ten users. Despite the smaller size of the dataset, the MuCBiT achieved a prediction accuracy of 86.25%, outperforming traditional machine learning models and other transformer-based classifiers for EMG signal classification. This integrative transformer-based gesture recognition promises notable advancements for ARH development, underscoring prospective improvements in prosthetics and human–robot interaction.

Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view data and poor generalization to new viewpoints. We introduce BenchSeg, a novel multi-view food video segmentation dataset and benchmark. BenchSeg aggregates 55 dish scenes (from Nutrition5k, Vegetables & Fruits, MetaFood3D, and FoodKit) with 25,284 meticulously annotated frames, capturing each dish under free 360° camera motion. We evaluate a diverse set of 20 state-of-the-art segmentation models (e.g., SAM-based, transformer, CNN, and large multimodal) on the existing FoodSeg103 dataset and evaluate them (alone and combined with video-memory modules) on BenchSeg. Quantitative and qualitative results demonstrate that while standard image segmenters degrade sharply under novel viewpoints, memory-augmented methods maintain temporal consistency across frames. Our best model, based on a combination of SeTR-MLA+XMem2, outperforms prior work (e.g., improving over FoodMem by ~2.63% mAP), offering new insights into food segmentation and tracking for dietary analysis. In addition to frame-wise spatial accuracy, we introduce a dedicated temporal evaluation protocol that explicitly quantifies segmentation stability over time through continuity, flicker rate, and IoU drift metrics. This allows us to reveal failure modes that remain invisible under standard per-frame evaluations. We release BenchSeg to foster future research. The project page, including the dataset annotations and the food segmentation models, can be found at https://amughrabi.github.io/benchseg.