Older adults frequently face difficulties in activities of daily living (ADLs) due to age-related declines in strength, coordination, and perception. Myoelectric control provides an intuitive human–robot interface by translating muscle activity into assistive commands. However, its practical application is still challenged by signal annotation, multijoint coordination, and cross-task generalization. This study proposes a 3-level intelligent framework for multijoint upper-limb assistance based on electromyography (EMG) to support the daily living activities of older adults. At the physiological level, situation-aware labeling protocols matched to different EMG conditions are proposed to reduce annotation ambiguity and improve robustness to signal changes. At the functional level, focusing on elemental joint activities, a deep backbone model is designed to infer both single-joint movements and coordinated multijoint patterns with an accuracy of 95.34%. At the behavioral level, the model is further distilled to support complex ADL tasks with human–robot interactions while continually incorporating new knowledge without catastrophic forgetting. The framework is implemented in real time on an EMG-controlled multijoint robotic system, providing smooth and coordinated assistance in daily activities. Overall, the proposed framework provides a systematic solution for EMG-based multijoint coordination, encompassing the entire pathway from physiological signal processing to functional intent decoding and behavioral adaptation during daily activities. It offers a technical approach to coordinated upper-limb assistance and lays a broader foundation for the design of practical and adaptive assistive systems, contributing to improved autonomy for older adults and supporting the broader societal goal of healthy aging.

The development of strain sensors with both superior sensitivity (gauge factor (GF) > 100) and broad strain-sensing range (> 50% strain) is still a grand challenge. Materials, which demonstrate significant structural deformation under microscale motion, are required to offer high sensitivity. Structural connection of materials upon large-scale motion is demanded to widen strain-sensing range. However, it is hard to achieve both features simultaneously. Herein, we design a crepe roll structure-inspired textile yarn-based strain sensor with one-dimensional (1D)-two-dimensional (2D) nanohybrid strain-sensing sheath, which possesses superior stretchability. This ultrastretchable strain sensor exhibits a wide and stable strain-sensing range from micro-scale to large-scale (0.01%–125%), and superior sensitivity (GF of 139.6 and 198.8 at 0.01% and 125%, respectively) simultaneously. The strain sensor is structurally constructed by a superelastic 1D-structured core elastomer polyurethane yarn (PUY), a novel high conductive crepe roll-structured (CRS) 1D-2D nanohybrid multilayer sheath which assembled by 1D nanomaterials silver nanowires (AgNWs) working as bridges to connect adjacent layers and 2D nanomaterials graphene nanoplates (GNPs) offering brittle lamellar structure, and a thin polydopamine (PDA) wrapping layer providing protection in exterior environment. During the stretching/deformation process, microcracks originate and propagate in the GNPs lamellar structure enable resistance to change significantly, while AgNWs bridge adjacent GNPs to accommodate applied stress partially and boost strain. The 1D crepe roll structure-inspired strain sensor demonstrates multifunctionality in multiscale deformative motion detection, such as respiratory motions of Sprague–Dawleyw rat, flexible digital display, and proprioception of multi-joint finger bending and antagonistic flexion/extension motions of its flexible continuum body.